{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,) (5, 3)\n",
      "(1000, 1) (1000, 1000)\n"
     ]
    }
   ],
   "source": [
    "import vectorbt as vbt\n",
    "from vectorbt.portfolio.enums import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numba import njit\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "seed = 42\n",
    "\n",
    "price = pd.Series([1., 2., 3., 4., 5.], index=pd.Index([\n",
    "    datetime(2020, 1, 1),\n",
    "    datetime(2020, 1, 2),\n",
    "    datetime(2020, 1, 3),\n",
    "    datetime(2020, 1, 4),\n",
    "    datetime(2020, 1, 5)\n",
    "]))\n",
    "price_wide = price.vbt.tile(3, keys=['a', 'b', 'c'])\n",
    "print(price.shape, price_wide.shape)\n",
    "\n",
    "big_price = pd.DataFrame(np.random.uniform(0.9, 1.1, size=(1000,)))\n",
    "big_price.index = [datetime(2018, 1, 1) + timedelta(days=i) for i in range(1000)]\n",
    "big_price_wide = big_price.vbt.tile(1000)\n",
    "print(big_price.shape, big_price_wide.shape)\n",
    "\n",
    "# Disable caching for performance testing\n",
    "# NOTE: Expect waterfall of executions, since some attributes depend on other attributes \n",
    "# that have to be calculated again and again\n",
    "vbt.settings.caching['enabled'] = False\n",
    "vbt.settings.portfolio['attach_call_seq'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## from_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_size = pd.Series([np.inf, -np.inf, np.nan, np.inf, -np.inf], index=price.index)\n",
    "order_size_wide = order_size.vbt.tile(3, keys=['a', 'b', 'c'])\n",
    "\n",
    "big_order_size = pd.DataFrame.vbt.empty((1000,), 1, dtype=np.float64)\n",
    "big_order_size.iloc[1::2] = -1\n",
    "big_order_size_wide = big_order_size.vbt.tile(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.24 ms ± 179 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_orders(big_price, big_order_size, init_cash=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67 ms ± 2.3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_orders(big_price_wide, big_order_size_wide, init_cash=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64.3 ms ± 1.05 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_orders(\\\n",
    "    big_price_wide, big_order_size_wide, init_cash=np.inf, \\\n",
    "    group_by=np.repeat(np.arange(500), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65.9 ms ± 1.68 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_orders(\\\n",
    "    big_price_wide, big_order_size_wide, init_cash=np.inf, \\\n",
    "    group_by=np.repeat(np.arange(500), 2), cash_sharing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71.7 ms ± 510 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_orders(\\\n",
    "    big_price_wide, big_order_size_wide, init_cash=np.inf, \\\n",
    "    group_by=np.repeat(np.arange(500), 2), cash_sharing=True, call_seq=CallSeqType.Auto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66.3 ms ± 285 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_orders(\\\n",
    "    big_price_wide, big_order_size_wide, init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63.7 ms ± 728 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_orders(\\\n",
    "    big_price_wide, big_order_size_wide, init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0), cash_sharing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68.3 ms ± 443 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_orders(\\\n",
    "    big_price_wide, big_order_size_wide, init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0), cash_sharing=True, call_seq=CallSeqType.Auto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_per_direction(price=price, size=order_size, **kwargs):\n",
    "    print('Both')\n",
    "    try:\n",
    "        portfolio = vbt.Portfolio.from_orders(price, size, direction='both', **kwargs)\n",
    "        print(portfolio.orders.records)\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "    print('LongOnly')\n",
    "    try:\n",
    "        portfolio = vbt.Portfolio.from_orders(price, size, direction='longonly', **kwargs)\n",
    "        print(portfolio.orders.records)\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "    print('ShortOnly')\n",
    "    try:\n",
    "        portfolio = vbt.Portfolio.from_orders(price, size, direction='shortonly', **kwargs)\n",
    "        print(portfolio.orders.records)\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    1    0  200.0    2.0   0.0     1\n",
      "2   2    3    0  100.0    4.0   0.0     0\n",
      "LongOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    1    0  100.0    2.0   0.0     1\n",
      "2   2    3    0   50.0    4.0   0.0     0\n",
      "3   3    4    0   50.0    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     1\n",
      "1   1    1    0  100.0    2.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    1    0  200.0    2.0   0.0     1\n",
      "2   2    3    0  100.0    4.0   0.0     0\n",
      "3   3    0    1  100.0    1.0   0.0     0\n",
      "4   4    1    1  200.0    2.0   0.0     1\n",
      "5   5    3    1  100.0    4.0   0.0     0\n",
      "6   6    0    2  100.0    1.0   0.0     0\n",
      "7   7    1    2  200.0    2.0   0.0     1\n",
      "8   8    3    2  100.0    4.0   0.0     0\n",
      "LongOnly\n",
      "    id  idx  col   size  price  fees  side\n",
      "0    0    0    0  100.0    1.0   0.0     0\n",
      "1    1    1    0  100.0    2.0   0.0     1\n",
      "2    2    3    0   50.0    4.0   0.0     0\n",
      "3    3    4    0   50.0    5.0   0.0     1\n",
      "4    4    0    1  100.0    1.0   0.0     0\n",
      "5    5    1    1  100.0    2.0   0.0     1\n",
      "6    6    3    1   50.0    4.0   0.0     0\n",
      "7    7    4    1   50.0    5.0   0.0     1\n",
      "8    8    0    2  100.0    1.0   0.0     0\n",
      "9    9    1    2  100.0    2.0   0.0     1\n",
      "10  10    3    2   50.0    4.0   0.0     0\n",
      "11  11    4    2   50.0    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     1\n",
      "1   1    1    0  100.0    2.0   0.0     0\n",
      "2   2    0    1  100.0    1.0   0.0     1\n",
      "3   3    1    1  100.0    2.0   0.0     0\n",
      "4   4    0    2  100.0    1.0   0.0     1\n",
      "5   5    1    2  100.0    2.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(price=price_wide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    1  100.0    1.0   0.0     1\n",
      "LongOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "ShortOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     1\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=[[np.inf, -np.inf]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_size_one = pd.Series([1, -1, np.nan, 1, -1], index=price.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col        size  price  fees  side\n",
      "0   0    0    0   99.009901   1.01   0.0     0\n",
      "1   1    1    0  198.019802   2.02   0.0     1\n",
      "2   2    3    0   99.009901   4.04   0.0     0\n",
      "LongOnly\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  99.009901   1.01   0.0     0\n",
      "1   1    1    0  99.009901   2.02   0.0     1\n",
      "2   2    3    0  49.504950   4.04   0.0     0\n",
      "3   3    4    0  49.504950   5.05   0.0     1\n",
      "ShortOnly\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  99.009901   1.01   0.0     1\n",
      "1   1    1    0  99.009901   2.02   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(price=price * 1.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   1.0    1.0   0.0     0\n",
      "1    1    1    0   1.0    2.0   0.0     1\n",
      "2    2    3    0   1.0    4.0   0.0     0\n",
      "3    3    4    0   1.0    5.0   0.0     1\n",
      "4    4    0    1   1.0    1.0   0.1     0\n",
      "5    5    1    1   1.0    2.0   0.2     1\n",
      "6    6    3    1   1.0    4.0   0.4     0\n",
      "7    7    4    1   1.0    5.0   0.5     1\n",
      "8    8    0    2   1.0    1.0   1.0     0\n",
      "9    9    1    2   1.0    2.0   2.0     1\n",
      "10  10    3    2   1.0    4.0   4.0     0\n",
      "11  11    4    2   1.0    5.0   5.0     1\n",
      "LongOnly\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   1.0    1.0   0.0     0\n",
      "1    1    1    0   1.0    2.0   0.0     1\n",
      "2    2    3    0   1.0    4.0   0.0     0\n",
      "3    3    4    0   1.0    5.0   0.0     1\n",
      "4    4    0    1   1.0    1.0   0.1     0\n",
      "5    5    1    1   1.0    2.0   0.2     1\n",
      "6    6    3    1   1.0    4.0   0.4     0\n",
      "7    7    4    1   1.0    5.0   0.5     1\n",
      "8    8    0    2   1.0    1.0   1.0     0\n",
      "9    9    1    2   1.0    2.0   2.0     1\n",
      "10  10    3    2   1.0    4.0   4.0     0\n",
      "11  11    4    2   1.0    5.0   5.0     1\n",
      "ShortOnly\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   1.0    1.0   0.0     1\n",
      "1    1    1    0   1.0    2.0   0.0     0\n",
      "2    2    3    0   1.0    4.0   0.0     1\n",
      "3    3    4    0   1.0    5.0   0.0     0\n",
      "4    4    0    1   1.0    1.0   0.1     1\n",
      "5    5    1    1   1.0    2.0   0.2     0\n",
      "6    6    3    1   1.0    4.0   0.4     1\n",
      "7    7    4    1   1.0    5.0   0.5     0\n",
      "8    8    0    2   1.0    1.0   1.0     1\n",
      "9    9    1    2   1.0    2.0   2.0     0\n",
      "10  10    3    2   1.0    4.0   4.0     1\n",
      "11  11    4    2   1.0    5.0   5.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=order_size_one, fees=[[0., 0.1, 1.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   1.0    1.0   0.0     0\n",
      "1    1    1    0   1.0    2.0   0.0     1\n",
      "2    2    3    0   1.0    4.0   0.0     0\n",
      "3    3    4    0   1.0    5.0   0.0     1\n",
      "4    4    0    1   1.0    1.0   0.1     0\n",
      "5    5    1    1   1.0    2.0   0.1     1\n",
      "6    6    3    1   1.0    4.0   0.1     0\n",
      "7    7    4    1   1.0    5.0   0.1     1\n",
      "8    8    0    2   1.0    1.0   1.0     0\n",
      "9    9    1    2   1.0    2.0   1.0     1\n",
      "10  10    3    2   1.0    4.0   1.0     0\n",
      "11  11    4    2   1.0    5.0   1.0     1\n",
      "LongOnly\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   1.0    1.0   0.0     0\n",
      "1    1    1    0   1.0    2.0   0.0     1\n",
      "2    2    3    0   1.0    4.0   0.0     0\n",
      "3    3    4    0   1.0    5.0   0.0     1\n",
      "4    4    0    1   1.0    1.0   0.1     0\n",
      "5    5    1    1   1.0    2.0   0.1     1\n",
      "6    6    3    1   1.0    4.0   0.1     0\n",
      "7    7    4    1   1.0    5.0   0.1     1\n",
      "8    8    0    2   1.0    1.0   1.0     0\n",
      "9    9    1    2   1.0    2.0   1.0     1\n",
      "10  10    3    2   1.0    4.0   1.0     0\n",
      "11  11    4    2   1.0    5.0   1.0     1\n",
      "ShortOnly\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   1.0    1.0   0.0     1\n",
      "1    1    1    0   1.0    2.0   0.0     0\n",
      "2    2    3    0   1.0    4.0   0.0     1\n",
      "3    3    4    0   1.0    5.0   0.0     0\n",
      "4    4    0    1   1.0    1.0   0.1     1\n",
      "5    5    1    1   1.0    2.0   0.1     0\n",
      "6    6    3    1   1.0    4.0   0.1     1\n",
      "7    7    4    1   1.0    5.0   0.1     0\n",
      "8    8    0    2   1.0    1.0   1.0     1\n",
      "9    9    1    2   1.0    2.0   1.0     0\n",
      "10  10    3    2   1.0    4.0   1.0     1\n",
      "11  11    4    2   1.0    5.0   1.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=order_size_one, fixed_fees=[[0., 0.1, 1.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   1.0    1.0   0.0     0\n",
      "1    1    1    0   1.0    2.0   0.0     1\n",
      "2    2    3    0   1.0    4.0   0.0     0\n",
      "3    3    4    0   1.0    5.0   0.0     1\n",
      "4    4    0    1   1.0    1.1   0.0     0\n",
      "5    5    1    1   1.0    1.8   0.0     1\n",
      "6    6    3    1   1.0    4.4   0.0     0\n",
      "7    7    4    1   1.0    4.5   0.0     1\n",
      "8    8    0    2   1.0    2.0   0.0     0\n",
      "9    9    1    2   1.0    0.0   0.0     1\n",
      "10  10    3    2   1.0    8.0   0.0     0\n",
      "11  11    4    2   1.0    0.0   0.0     1\n",
      "LongOnly\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   1.0    1.0   0.0     0\n",
      "1    1    1    0   1.0    2.0   0.0     1\n",
      "2    2    3    0   1.0    4.0   0.0     0\n",
      "3    3    4    0   1.0    5.0   0.0     1\n",
      "4    4    0    1   1.0    1.1   0.0     0\n",
      "5    5    1    1   1.0    1.8   0.0     1\n",
      "6    6    3    1   1.0    4.4   0.0     0\n",
      "7    7    4    1   1.0    4.5   0.0     1\n",
      "8    8    0    2   1.0    2.0   0.0     0\n",
      "9    9    1    2   1.0    0.0   0.0     1\n",
      "10  10    3    2   1.0    8.0   0.0     0\n",
      "11  11    4    2   1.0    0.0   0.0     1\n",
      "ShortOnly\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   1.0    1.0   0.0     1\n",
      "1    1    1    0   1.0    2.0   0.0     0\n",
      "2    2    3    0   1.0    4.0   0.0     1\n",
      "3    3    4    0   1.0    5.0   0.0     0\n",
      "4    4    0    1   1.0    0.9   0.0     1\n",
      "5    5    1    1   1.0    2.2   0.0     0\n",
      "6    6    3    1   1.0    3.6   0.0     1\n",
      "7    7    4    1   1.0    5.5   0.0     0\n",
      "8    8    0    2   1.0    0.0   0.0     1\n",
      "9    9    1    2   1.0    4.0   0.0     0\n",
      "10  10    3    2   1.0    0.0   0.0     1\n",
      "11  11    4    2   1.0   10.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=order_size_one, slippage=[[0., 0.1, 1.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    1    0   1.0    2.0   0.0     1\n",
      "2   2    3    0   1.0    4.0   0.0     0\n",
      "3   3    4    0   1.0    5.0   0.0     1\n",
      "4   4    0    1   1.0    1.0   0.0     0\n",
      "5   5    1    1   1.0    2.0   0.0     1\n",
      "6   6    3    1   1.0    4.0   0.0     0\n",
      "7   7    4    1   1.0    5.0   0.0     1\n",
      "LongOnly\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    1    0   1.0    2.0   0.0     1\n",
      "2   2    3    0   1.0    4.0   0.0     0\n",
      "3   3    4    0   1.0    5.0   0.0     1\n",
      "4   4    0    1   1.0    1.0   0.0     0\n",
      "5   5    1    1   1.0    2.0   0.0     1\n",
      "6   6    3    1   1.0    4.0   0.0     0\n",
      "7   7    4    1   1.0    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     1\n",
      "1   1    1    0   1.0    2.0   0.0     0\n",
      "2   2    3    0   1.0    4.0   0.0     1\n",
      "3   3    4    0   1.0    5.0   0.0     0\n",
      "4   4    0    1   1.0    1.0   0.0     1\n",
      "5   5    1    1   1.0    2.0   0.0     0\n",
      "6   6    3    1   1.0    4.0   0.0     1\n",
      "7   7    4    1   1.0    5.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=order_size_one, min_size=[[0., 1., 2.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   0.5    1.0   0.0     0\n",
      "1    1    1    0   0.5    2.0   0.0     1\n",
      "2    2    3    0   0.5    4.0   0.0     0\n",
      "3    3    4    0   0.5    5.0   0.0     1\n",
      "4    4    0    1   1.0    1.0   0.0     0\n",
      "5    5    1    1   1.0    2.0   0.0     1\n",
      "6    6    3    1   1.0    4.0   0.0     0\n",
      "7    7    4    1   1.0    5.0   0.0     1\n",
      "8    8    0    2   1.0    1.0   0.0     0\n",
      "9    9    1    2   1.0    2.0   0.0     1\n",
      "10  10    3    2   1.0    4.0   0.0     0\n",
      "11  11    4    2   1.0    5.0   0.0     1\n",
      "LongOnly\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   0.5    1.0   0.0     0\n",
      "1    1    1    0   0.5    2.0   0.0     1\n",
      "2    2    3    0   0.5    4.0   0.0     0\n",
      "3    3    4    0   0.5    5.0   0.0     1\n",
      "4    4    0    1   1.0    1.0   0.0     0\n",
      "5    5    1    1   1.0    2.0   0.0     1\n",
      "6    6    3    1   1.0    4.0   0.0     0\n",
      "7    7    4    1   1.0    5.0   0.0     1\n",
      "8    8    0    2   1.0    1.0   0.0     0\n",
      "9    9    1    2   1.0    2.0   0.0     1\n",
      "10  10    3    2   1.0    4.0   0.0     0\n",
      "11  11    4    2   1.0    5.0   0.0     1\n",
      "ShortOnly\n",
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    0   0.5    1.0   0.0     1\n",
      "1    1    1    0   0.5    2.0   0.0     0\n",
      "2    2    3    0   0.5    4.0   0.0     1\n",
      "3    3    4    0   0.5    5.0   0.0     0\n",
      "4    4    0    1   1.0    1.0   0.0     1\n",
      "5    5    1    1   1.0    2.0   0.0     0\n",
      "6    6    3    1   1.0    4.0   0.0     1\n",
      "7    7    4    1   1.0    5.0   0.0     0\n",
      "8    8    0    2   1.0    1.0   0.0     1\n",
      "9    9    1    2   1.0    2.0   0.0     0\n",
      "10  10    3    2   1.0    4.0   0.0     1\n",
      "11  11    4    2   1.0    5.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=order_size_one, max_size=[[0.5, 1., np.inf]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    1    0   1.0    2.0   0.0     1\n",
      "2   2    3    0   1.0    4.0   0.0     0\n",
      "3   3    4    0   1.0    5.0   0.0     1\n",
      "4   4    1    1   1.0    2.0   0.0     1\n",
      "5   5    3    1   1.0    4.0   0.0     0\n",
      "6   6    4    1   1.0    5.0   0.0     1\n",
      "LongOnly\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    1    0   1.0    2.0   0.0     1\n",
      "2   2    3    0   1.0    4.0   0.0     0\n",
      "3   3    4    0   1.0    5.0   0.0     1\n",
      "4   4    3    1   1.0    4.0   0.0     0\n",
      "5   5    4    1   1.0    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     1\n",
      "1   1    1    0   1.0    2.0   0.0     0\n",
      "2   2    3    0   1.0    4.0   0.0     1\n",
      "3   3    4    0   1.0    5.0   0.0     0\n",
      "4   4    3    1   1.0    4.0   0.0     1\n",
      "5   5    4    1   1.0    5.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=order_size_one, reject_prob=[[0., 0.5, 1.]], seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "__init__() got an unexpected keyword argument 'close_first'\n",
      "LongOnly\n",
      "__init__() got an unexpected keyword argument 'close_first'\n",
      "ShortOnly\n",
      "__init__() got an unexpected keyword argument 'close_first'\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=pd.Series([np.inf, -np.inf, -np.inf, np.inf, np.inf]), close_first=[[False, True]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col    size  price  fees  side\n",
      "0   0    0    0   100.0    1.0   0.0     0\n",
      "1   1    1    0  1000.0    2.0   0.0     1\n",
      "2   2    3    0   500.0    4.0   0.0     0\n",
      "3   3    4    0  1000.0    5.0   0.0     1\n",
      "4   4    1    1  1000.0    2.0   0.0     1\n",
      "5   5    4    1  1000.0    5.0   0.0     1\n",
      "LongOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    1    0  100.0    2.0   0.0     1\n",
      "2   2    3    0   50.0    4.0   0.0     0\n",
      "3   3    4    0   50.0    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  idx  col    size  price  fees  side\n",
      "0   0    0    0  1000.0    1.0   0.0     1\n",
      "1   1    1    0   550.0    2.0   0.0     0\n",
      "2   2    3    0  1000.0    4.0   0.0     1\n",
      "3   3    4    0   800.0    5.0   0.0     0\n",
      "4   4    0    1  1000.0    1.0   0.0     1\n",
      "5   5    3    1  1000.0    4.0   0.0     1\n",
      "6   6    4    1  1000.0    5.0   0.0     0\n",
      "All\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    1    0  200.0    2.0   0.0     1\n",
      "2   2    3    0  100.0    4.0   0.0     0\n",
      "3   3    0    1  100.0    1.0   0.0     0\n",
      "4   4    1    1  200.0    2.0   0.0     1\n",
      "5   5    3    1  100.0    4.0   0.0     0\n",
      "LongOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    1    0  100.0    2.0   0.0     1\n",
      "2   2    3    0   50.0    4.0   0.0     0\n",
      "3   3    4    0   50.0    5.0   0.0     1\n",
      "4   4    0    1  100.0    1.0   0.0     0\n",
      "5   5    1    1  100.0    2.0   0.0     1\n",
      "6   6    3    1   50.0    4.0   0.0     0\n",
      "7   7    4    1   50.0    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     1\n",
      "1   1    1    0  100.0    2.0   0.0     0\n",
      "2   2    0    1  100.0    1.0   0.0     1\n",
      "3   3    1    1  100.0    2.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=order_size_one * 1000, allow_partial=[[True, False]])\n",
    "test_per_direction(size=order_size, allow_partial=[[True, False]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col    size  price  fees  side\n",
      "0   0    0    0   100.0    1.0   0.0     0\n",
      "1   1    1    0  1000.0    2.0   0.0     1\n",
      "2   2    3    0   500.0    4.0   0.0     0\n",
      "3   3    4    0  1000.0    5.0   0.0     1\n",
      "LongOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    1    0  100.0    2.0   0.0     1\n",
      "2   2    3    0   50.0    4.0   0.0     0\n",
      "3   3    4    0   50.0    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  idx  col    size  price  fees  side\n",
      "0   0    0    0  1000.0    1.0   0.0     1\n",
      "1   1    1    0   550.0    2.0   0.0     0\n",
      "2   2    3    0  1000.0    4.0   0.0     1\n",
      "3   3    4    0   800.0    5.0   0.0     0\n",
      "All\n",
      "Final size is less than requested\n",
      "LongOnly\n",
      "Final size is less than requested\n",
      "ShortOnly\n",
      "Final size is less than requested\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=order_size_one * 1000, allow_partial=True, raise_reject=True)\n",
    "test_per_direction(size=order_size_one * 1000, allow_partial=False, raise_reject=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col  group   cash  position   debt  free_cash  val_price  value  \\\n",
      "0   0    0    0      0  100.0       0.0    0.0      100.0        1.0  100.0   \n",
      "1   1    1    0      0    0.0     100.0    0.0        0.0        2.0  200.0   \n",
      "2   2    2    0      0  400.0    -100.0  200.0        0.0        3.0  100.0   \n",
      "3   3    3    0      0  400.0    -100.0  200.0        0.0        4.0    0.0   \n",
      "4   4    4    0      0    0.0       0.0    0.0        0.0        5.0    0.0   \n",
      "\n",
      "   ...  new_free_cash  new_val_price  new_value  res_size  res_price  \\\n",
      "0  ...            0.0            1.0      100.0     100.0        1.0   \n",
      "1  ...            0.0            2.0      200.0     200.0        2.0   \n",
      "2  ...            0.0            3.0      100.0       NaN        NaN   \n",
      "3  ...            0.0            4.0        0.0     100.0        4.0   \n",
      "4  ...            0.0            5.0        0.0       NaN        NaN   \n",
      "\n",
      "   res_fees  res_side  res_status  res_status_info  order_id  \n",
      "0       0.0         0           0               -1         0  \n",
      "1       0.0         1           0               -1         1  \n",
      "2       NaN        -1           1                0        -1  \n",
      "3       0.0         0           0               -1         2  \n",
      "4       NaN        -1           2                6        -1  \n",
      "\n",
      "[5 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_orders(price, order_size, log=True, direction='both')\n",
    "\n",
    "print(portfolio.logs.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    1    0  200.0    2.0   0.0     1\n",
      "2   2    3    0  100.0    4.0   0.0     0\n",
      "3   3    0    1  100.0    1.0   0.0     0\n",
      "4   4    1    1  200.0    2.0   0.0     1\n",
      "5   5    3    1  100.0    4.0   0.0     0\n",
      "6   6    0    2  100.0    1.0   0.0     0\n",
      "7   7    1    2  200.0    2.0   0.0     1\n",
      "8   8    3    2  100.0    4.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_wide, order_size, \n",
    "    group_by=np.array([0, 0, 1]), direction='both')\n",
    "\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    1    0  200.0    2.0   0.0     1\n",
      "2   2    3    0  100.0    4.0   0.0     0\n",
      "3   3    0    2  100.0    1.0   0.0     0\n",
      "4   4    1    2  200.0    2.0   0.0     1\n",
      "5   5    3    2  100.0    4.0   0.0     0\n",
      "            a  b  c\n",
      "2020-01-01  0  1  0\n",
      "2020-01-02  0  1  0\n",
      "2020-01-03  0  1  0\n",
      "2020-01-04  0  1  0\n",
      "2020-01-05  0  1  0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_wide, order_size, \n",
    "    group_by=np.array([0, 0, 1]), cash_sharing=True, direction='both')\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.call_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    1  100.0    1.0   0.0     0\n",
      "1   1    1    1  200.0    2.0   0.0     1\n",
      "2   2    3    1  100.0    4.0   0.0     0\n",
      "3   3    0    2  100.0    1.0   0.0     0\n",
      "4   4    1    2  200.0    2.0   0.0     1\n",
      "5   5    3    2  100.0    4.0   0.0     0\n",
      "            a  b  c\n",
      "2020-01-01  1  0  0\n",
      "2020-01-02  1  0  0\n",
      "2020-01-03  1  0  0\n",
      "2020-01-04  1  0  0\n",
      "2020-01-05  1  0  0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_wide, order_size, \n",
    "    group_by=np.array([0, 0, 1]), cash_sharing=True, direction='both', call_seq='reversed')\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.call_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    1    0  200.0    2.0   0.0     1\n",
      "2   2    3    1  100.0    4.0   0.0     0\n",
      "3   3    4    1  120.0    5.0   0.0     1\n",
      "4   4    0    2  100.0    1.0   0.0     0\n",
      "5   5    1    2  200.0    2.0   0.0     1\n",
      "6   6    3    2  100.0    4.0   0.0     0\n",
      "            a  b  c\n",
      "2020-01-01  0  1  0\n",
      "2020-01-02  0  1  0\n",
      "2020-01-03  0  1  0\n",
      "2020-01-04  1  0  0\n",
      "2020-01-05  0  1  0\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    1  100.0    1.0   0.0     0\n",
      "1   1    1    1  200.0    2.0   0.0     1\n",
      "2   2    3    1  100.0    4.0   0.0     0\n",
      "3   3    0    2  100.0    1.0   0.0     0\n",
      "4   4    1    2  200.0    2.0   0.0     1\n",
      "5   5    3    2  100.0    4.0   0.0     0\n",
      "            a  b  c\n",
      "2020-01-01  1  0  0\n",
      "2020-01-02  0  1  0\n",
      "2020-01-03  1  0  0\n",
      "2020-01-04  1  0  0\n",
      "2020-01-05  1  0  0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_wide, order_size, \n",
    "    group_by=np.array([0, 0, 1]), cash_sharing=True, direction='both', call_seq='random')\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.call_seq)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_wide, order_size, \n",
    "    group_by=np.array([0, 0, 1]), cash_sharing=True, direction='both', call_seq='random', seed=42)\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.call_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    2  100.0    1.0   0.0     0\n",
      "1   1    1    2  200.0    1.0   0.0     1\n",
      "2   2    1    1  200.0    1.0   0.0     0\n",
      "3   3    2    1  200.0    1.0   0.0     1\n",
      "4   4    2    0  200.0    1.0   0.0     0\n",
      "5   5    3    0  200.0    1.0   0.0     1\n",
      "6   6    3    2  200.0    1.0   0.0     0\n",
      "7   7    4    2  200.0    1.0   0.0     1\n",
      "8   8    4    1  200.0    1.0   0.0     0\n",
      "   0  1  2\n",
      "0  0  1  2\n",
      "1  2  0  1\n",
      "2  1  2  0\n",
      "3  0  1  2\n",
      "4  2  0  1\n",
      "       0      1      2\n",
      "0    0.0    0.0  100.0\n",
      "1    0.0  200.0 -100.0\n",
      "2  200.0    0.0 -100.0\n",
      "3    0.0    0.0  100.0\n",
      "4    0.0  200.0 -100.0\n",
      "0    100.0\n",
      "1    100.0\n",
      "2    100.0\n",
      "3    100.0\n",
      "4    100.0\n",
      "dtype: float64\n",
      "LongOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    2  100.0    1.0   0.0     0\n",
      "1   1    1    2  100.0    1.0   0.0     1\n",
      "2   2    1    1  100.0    1.0   0.0     0\n",
      "3   3    2    1  100.0    1.0   0.0     1\n",
      "4   4    2    0  100.0    1.0   0.0     0\n",
      "5   5    3    0  100.0    1.0   0.0     1\n",
      "6   6    3    2  100.0    1.0   0.0     0\n",
      "7   7    4    2  100.0    1.0   0.0     1\n",
      "8   8    4    1  100.0    1.0   0.0     0\n",
      "   0  1  2\n",
      "0  0  1  2\n",
      "1  2  0  1\n",
      "2  1  2  0\n",
      "3  0  1  2\n",
      "4  2  0  1\n",
      "       0      1      2\n",
      "0    0.0    0.0  100.0\n",
      "1    0.0  100.0    0.0\n",
      "2  100.0    0.0    0.0\n",
      "3    0.0    0.0  100.0\n",
      "4    0.0  100.0    0.0\n",
      "0    100.0\n",
      "1    100.0\n",
      "2    100.0\n",
      "3    100.0\n",
      "4    100.0\n",
      "dtype: float64\n",
      "ShortOnly\n",
      "   id  idx  col   size  price  fees  side\n",
      "0   0    0    2  100.0    1.0   0.0     1\n",
      "1   1    1    2  100.0    1.0   0.0     0\n",
      "2   2    2    0  100.0    1.0   0.0     1\n",
      "3   3    3    0  100.0    1.0   0.0     0\n",
      "4   4    4    1  100.0    1.0   0.0     1\n",
      "   0  1  2\n",
      "0  2  0  1\n",
      "1  1  0  2\n",
      "2  0  2  1\n",
      "3  2  1  0\n",
      "4  1  0  2\n",
      "       0      1      2\n",
      "0    0.0    0.0 -100.0\n",
      "1    0.0    0.0    0.0\n",
      "2 -100.0    0.0    0.0\n",
      "3    0.0    0.0    0.0\n",
      "4    0.0 -100.0    0.0\n",
      "0    100.0\n",
      "1    100.0\n",
      "2    100.0\n",
      "3    100.0\n",
      "4    100.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for direction in ('Both', 'LongOnly', 'ShortOnly'):\n",
    "    print(direction)\n",
    "    portfolio = vbt.Portfolio.from_orders(1., pd.DataFrame([\n",
    "        [0., 0., np.inf],\n",
    "        [0., np.inf, -np.inf],\n",
    "        [np.inf, -np.inf, 0.],\n",
    "        [-np.inf, 0., np.inf],\n",
    "        [0., np.inf, -np.inf],\n",
    "    ]), group_by=np.array([0, 0, 0]), \n",
    "        cash_sharing=True, call_seq='auto', direction=direction)\n",
    "\n",
    "    print(portfolio.orders.records)\n",
    "    print(portfolio.call_seq)\n",
    "    print(portfolio.assets())\n",
    "    print(portfolio.value())\n",
    "    # notice how shorting with np.inf only accounts for current cash, not for other assets!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0  75.0    1.0   0.0     0\n",
      "1   1    0    1  75.0    1.0   0.0     1\n",
      "LongOnly\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0  75.0    1.0   0.0     0\n",
      "ShortOnly\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0  75.0    1.0   0.0     1\n",
      "               0     1\n",
      "2020-01-01  75.0 -75.0\n",
      "2020-01-02  75.0 -75.0\n",
      "2020-01-03  75.0 -75.0\n",
      "2020-01-04  75.0 -75.0\n",
      "2020-01-05  75.0 -75.0\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0  75.0    1.0   0.0     0\n",
      "1   1    0    1  25.0    1.0   0.0     0\n",
      "               a     b    c\n",
      "2020-01-01  75.0  25.0  0.0\n",
      "2020-01-02  75.0  25.0  0.0\n",
      "2020-01-03  75.0  25.0  0.0\n",
      "2020-01-04  75.0  25.0  0.0\n",
      "2020-01-05  75.0  25.0  0.0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=[[75., -75.]], size_type=SizeType.TargetAmount)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price, [[75., -75.]], size_type=SizeType.TargetAmount, direction='both')\n",
    "print(portfolio.assets())\n",
    "\n",
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_wide, 75., size_type=SizeType.TargetAmount, \n",
    "    group_by=np.array([0, 0, 0]), cash_sharing=True, direction='both')\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.assets())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  50.000000    1.0   0.0     0\n",
      "1   1    1    0  25.000000    2.0   0.0     1\n",
      "2   2    2    0   8.333333    3.0   0.0     1\n",
      "3   3    3    0   4.166667    4.0   0.0     1\n",
      "4   4    4    0   2.500000    5.0   0.0     1\n",
      "5   5    0    1  50.000000    1.0   0.0     1\n",
      "6   6    1    1  25.000000    2.0   0.0     0\n",
      "7   7    2    1   8.333333    3.0   0.0     0\n",
      "8   8    3    1   4.166667    4.0   0.0     0\n",
      "9   9    4    1   2.500000    5.0   0.0     0\n",
      "LongOnly\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  50.000000    1.0   0.0     0\n",
      "1   1    1    0  25.000000    2.0   0.0     1\n",
      "2   2    2    0   8.333333    3.0   0.0     1\n",
      "3   3    3    0   4.166667    4.0   0.0     1\n",
      "4   4    4    0   2.500000    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  50.000000    1.0   0.0     1\n",
      "1   1    1    0  25.000000    2.0   0.0     0\n",
      "2   2    2    0   8.333333    3.0   0.0     0\n",
      "3   3    3    0   4.166667    4.0   0.0     0\n",
      "4   4    4    0   2.500000    5.0   0.0     0\n",
      "               0     1\n",
      "2020-01-01  50.0 -50.0\n",
      "2020-01-02  50.0 -50.0\n",
      "2020-01-03  50.0 -50.0\n",
      "2020-01-04  50.0 -50.0\n",
      "2020-01-05  50.0 -50.0\n",
      "    id  idx  col       size  price  fees  side\n",
      "0    0    0    0  50.000000    1.0   0.0     0\n",
      "1    1    0    1  50.000000    1.0   0.0     0\n",
      "2    2    1    0  25.000000    2.0   0.0     1\n",
      "3    3    1    1  25.000000    2.0   0.0     1\n",
      "4    4    1    2  25.000000    2.0   0.0     0\n",
      "5    5    2    0   8.333333    3.0   0.0     1\n",
      "6    6    2    1   8.333333    3.0   0.0     1\n",
      "7    7    2    2   8.333333    3.0   0.0     1\n",
      "8    8    3    0   4.166667    4.0   0.0     1\n",
      "9    9    3    1   4.166667    4.0   0.0     1\n",
      "10  10    3    2   4.166667    4.0   0.0     1\n",
      "11  11    4    0   2.500000    5.0   0.0     1\n",
      "12  12    4    1   2.500000    5.0   0.0     1\n",
      "13  13    4    2   2.500000    5.0   0.0     1\n",
      "               a     b     c\n",
      "2020-01-01  50.0  50.0   0.0\n",
      "2020-01-02  50.0  50.0  50.0\n",
      "2020-01-03  50.0  50.0  50.0\n",
      "2020-01-04  50.0  50.0  50.0\n",
      "2020-01-05  50.0  50.0  50.0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=[[50., -50.]], size_type=SizeType.TargetValue)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price, [[50., -50.]], size_type=SizeType.TargetValue, direction='both')\n",
    "print(portfolio.asset_value())\n",
    "\n",
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_wide, 50., size_type=SizeType.TargetValue, \n",
    "    group_by=np.array([0, 0, 0]), cash_sharing=True, direction='both')\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.asset_value(group_by=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  50.000000    1.0   0.0     0\n",
      "1   1    1    0  12.500000    2.0   0.0     1\n",
      "2   2    2    0   6.250000    3.0   0.0     1\n",
      "3   3    3    0   3.906250    4.0   0.0     1\n",
      "4   4    4    0   2.734375    5.0   0.0     1\n",
      "5   5    0    1  50.000000    1.0   0.0     1\n",
      "6   6    1    1  37.500000    2.0   0.0     0\n",
      "7   7    2    1   6.250000    3.0   0.0     0\n",
      "8   8    3    1   2.343750    4.0   0.0     0\n",
      "9   9    4    1   1.171875    5.0   0.0     0\n",
      "LongOnly\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  50.000000    1.0   0.0     0\n",
      "1   1    1    0  12.500000    2.0   0.0     1\n",
      "2   2    2    0   6.250000    3.0   0.0     1\n",
      "3   3    3    0   3.906250    4.0   0.0     1\n",
      "4   4    4    0   2.734375    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  50.000000    1.0   0.0     1\n",
      "1   1    1    0  37.500000    2.0   0.0     0\n",
      "2   2    2    0   6.250000    3.0   0.0     0\n",
      "3   3    3    0   2.343750    4.0   0.0     0\n",
      "4   4    4    0   1.171875    5.0   0.0     0\n",
      "              0    1\n",
      "2020-01-01  0.5 -0.5\n",
      "2020-01-02  0.5 -0.5\n",
      "2020-01-03  0.5 -0.5\n",
      "2020-01-04  0.5 -0.5\n",
      "2020-01-05  0.5 -0.5\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0  50.0    1.0   0.0     0\n",
      "1   1    0    1  50.0    1.0   0.0     0\n",
      "                   a         b    c\n",
      "2020-01-01  0.500000  0.500000  0.0\n",
      "2020-01-02  0.666667  0.666667  0.0\n",
      "2020-01-03  0.750000  0.750000  0.0\n",
      "2020-01-04  0.800000  0.800000  0.0\n",
      "2020-01-05  0.833333  0.833333  0.0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=[[0.5, -0.5]], size_type=SizeType.TargetPercent)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price, [[0.5, -0.5]], size_type=SizeType.TargetPercent, direction='both')\n",
    "print(portfolio.asset_value() / portfolio.value())\n",
    "\n",
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_wide, 0.5, size_type=SizeType.TargetPercent, \n",
    "    group_by=np.array([0, 0, 0]), cash_sharing=True, direction='both')\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.asset_value(group_by=False) / portfolio.value(group_by=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               a     b     c\n",
      "2020-01-01   0.0  30.0  70.0\n",
      "2020-01-02  70.0   0.0  30.0\n",
      "2020-01-03  30.0  70.0   0.0\n",
      "2020-01-04   0.0  30.0  70.0\n",
      "2020-01-05  70.0  30.0   0.0\n"
     ]
    }
   ],
   "source": [
    "price_one = pd.Series([1., 1., 1., 1., 1.], index=price.index)\n",
    "target_hold_value = pd.DataFrame({\n",
    "    'a': [0., 70., 30., 0., 70.],\n",
    "    'b': [30., 0., 70., 30., 30.],\n",
    "    'c': [70., 30., 0., 70., 0.]\n",
    "}, index=price.index)\n",
    "print(target_hold_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    1  30.0    1.0   0.0     0\n",
      "1   1    0    2  70.0    1.0   0.0     0\n",
      "2   2    1    2  40.0    1.0   0.0     1\n",
      "3   3    1    1  30.0    1.0   0.0     1\n",
      "4   4    2    1  70.0    1.0   0.0     0\n",
      "5   5    2    2  30.0    1.0   0.0     1\n",
      "6   6    3    2  30.0    1.0   0.0     0\n",
      "7   7    3    1  40.0    1.0   0.0     1\n",
      "8   8    4    0  40.0    1.0   0.0     0\n",
      "9   9    4    2  30.0    1.0   0.0     1\n",
      "               a     b     c\n",
      "2020-01-01   0.0  30.0  70.0\n",
      "2020-01-02   0.0 -30.0 -40.0\n",
      "2020-01-03   0.0  70.0 -30.0\n",
      "2020-01-04   0.0 -40.0  30.0\n",
      "2020-01-05  40.0   0.0 -30.0\n",
      "               a     b     c\n",
      "2020-01-01   0.0  30.0  70.0\n",
      "2020-01-02   0.0   0.0  30.0\n",
      "2020-01-03   0.0  70.0   0.0\n",
      "2020-01-04   0.0  30.0  30.0\n",
      "2020-01-05  40.0  30.0   0.0\n",
      "            a  b  c\n",
      "2020-01-01  1  2  0\n",
      "2020-01-02  0  2  1\n",
      "2020-01-03  1  0  2\n",
      "2020-01-04  0  2  1\n",
      "2020-01-05  1  0  2\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_one, target_hold_value, size_type=SizeType.TargetValue,\n",
    "    group_by=np.array([0, 0, 0]), cash_sharing=True,\n",
    "    call_seq='random', seed=43)\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.asset_flow())\n",
    "print(portfolio.asset_value(group_by=False))  # order fixed -> cannot rebalance properly\n",
    "print(portfolio.call_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    1  30.0    1.0   0.0     0\n",
      "1    1    0    2  70.0    1.0   0.0     0\n",
      "2    2    1    2  40.0    1.0   0.0     1\n",
      "3    3    1    1  30.0    1.0   0.0     1\n",
      "4    4    1    0  70.0    1.0   0.0     0\n",
      "5    5    2    0  40.0    1.0   0.0     1\n",
      "6    6    2    2  30.0    1.0   0.0     1\n",
      "7    7    2    1  70.0    1.0   0.0     0\n",
      "8    8    3    1  40.0    1.0   0.0     1\n",
      "9    9    3    0  30.0    1.0   0.0     1\n",
      "10  10    3    2  70.0    1.0   0.0     0\n",
      "11  11    4    2  70.0    1.0   0.0     1\n",
      "12  12    4    0  70.0    1.0   0.0     0\n",
      "               a     b     c\n",
      "2020-01-01   0.0  30.0  70.0\n",
      "2020-01-02  70.0 -30.0 -40.0\n",
      "2020-01-03 -40.0  70.0 -30.0\n",
      "2020-01-04 -30.0 -40.0  70.0\n",
      "2020-01-05  70.0   0.0 -70.0\n",
      "               a     b     c\n",
      "2020-01-01   0.0  30.0  70.0\n",
      "2020-01-02  70.0   0.0  30.0\n",
      "2020-01-03  30.0  70.0   0.0\n",
      "2020-01-04   0.0  30.0  70.0\n",
      "2020-01-05  70.0  30.0   0.0\n",
      "            a  b  c\n",
      "2020-01-01  0  1  2\n",
      "2020-01-02  2  1  0\n",
      "2020-01-03  0  2  1\n",
      "2020-01-04  1  0  2\n",
      "2020-01-05  2  1  0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_one, target_hold_value, size_type=SizeType.TargetValue, \n",
    "    group_by=np.array([0, 0, 0]), cash_sharing=True,\n",
    "    call_seq='auto')\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.asset_flow())\n",
    "print(portfolio.asset_value(group_by=False))  # order dynamic -> can rebalance\n",
    "print(portfolio.call_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    id  idx  col  size  price  fees  side\n",
      "0    0    0    1  30.0    1.0   0.0     0\n",
      "1    1    0    2  70.0    1.0   0.0     0\n",
      "2    2    1    2  40.0    1.0   0.0     1\n",
      "3    3    1    1  30.0    1.0   0.0     1\n",
      "4    4    1    0  70.0    1.0   0.0     0\n",
      "5    5    2    0  40.0    1.0   0.0     1\n",
      "6    6    2    2  30.0    1.0   0.0     1\n",
      "7    7    2    1  70.0    1.0   0.0     0\n",
      "8    8    3    1  40.0    1.0   0.0     1\n",
      "9    9    3    0  30.0    1.0   0.0     1\n",
      "10  10    3    2  70.0    1.0   0.0     0\n",
      "11  11    4    2  70.0    1.0   0.0     1\n",
      "12  12    4    0  70.0    1.0   0.0     0\n",
      "               a     b     c\n",
      "2020-01-01   0.0  30.0  70.0\n",
      "2020-01-02  70.0 -30.0 -40.0\n",
      "2020-01-03 -40.0  70.0 -30.0\n",
      "2020-01-04 -30.0 -40.0  70.0\n",
      "2020-01-05  70.0   0.0 -70.0\n",
      "               a     b     c\n",
      "2020-01-01   0.0  30.0  70.0\n",
      "2020-01-02  70.0   0.0  30.0\n",
      "2020-01-03  30.0  70.0   0.0\n",
      "2020-01-04   0.0  30.0  70.0\n",
      "2020-01-05  70.0  30.0   0.0\n",
      "            a  b  c\n",
      "2020-01-01  0  1  2\n",
      "2020-01-02  2  1  0\n",
      "2020-01-03  0  2  1\n",
      "2020-01-04  1  0  2\n",
      "2020-01-05  2  1  0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_one, target_hold_value / 100., size_type=SizeType.TargetPercent, \n",
    "    group_by=np.array([0, 0, 0]), cash_sharing=True,\n",
    "    call_seq='auto')\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.asset_flow())\n",
    "print(portfolio.asset_value(group_by=False))\n",
    "print(portfolio.call_seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## from_signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,) (5, 3)\n",
      "(5,) (5, 3)\n",
      "(1000,) (1000, 1000)\n",
      "(1000,) (1000, 1000)\n"
     ]
    }
   ],
   "source": [
    "entries = pd.Series([True, True, True, False, False], index=price.index)\n",
    "entries_wide = entries.vbt.tile(3, keys=['a', 'b', 'c'])\n",
    "print(entries.shape, entries_wide.shape)\n",
    "\n",
    "exits = pd.Series([False, False, True, True, True], index=price.index)\n",
    "exits_wide = exits.vbt.tile(3, keys=['a', 'b', 'c'])\n",
    "print(exits.shape, exits_wide.shape)\n",
    "\n",
    "big_entries = pd.DataFrame.vbt.signals.empty((1000,), index=big_price.index)\n",
    "big_entries.iloc[0::2] = True\n",
    "big_entries_wide = big_entries.vbt.tile(1000)\n",
    "print(big_entries.shape, big_entries_wide.shape)\n",
    "\n",
    "big_exits = pd.DataFrame.vbt.signals.empty((1000,), index=big_price.index)\n",
    "big_exits.iloc[1::2] = True\n",
    "big_exits_wide = big_exits.vbt.tile(1000)\n",
    "print(big_exits.shape, big_exits_wide.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.72 ms ± 238 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_signals(\\\n",
    "    big_price, big_entries, big_exits,\\\n",
    "    size=1., init_cash=np.inf, accumulate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.1 ms ± 2.6 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_signals(\\\n",
    "    big_price_wide, big_entries_wide, big_exits_wide,\\\n",
    "    size=1., init_cash=np.inf, accumulate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104 ms ± 1.87 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_signals(\\\n",
    "    big_price_wide, big_entries_wide, big_exits_wide,\\\n",
    "    size=1., init_cash=np.inf, sl_stop=np.inf, tp_stop=np.inf, accumulate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "171 ms ± 6.43 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_signals(\\\n",
    "    big_price_wide, big_entries_wide, big_exits_wide,\\\n",
    "    size=1., init_cash=np.inf, accumulate=True, log=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98.4 ms ± 250 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_signals(\\\n",
    "    big_price_wide, big_entries_wide, big_exits_wide,\\\n",
    "    size=1., init_cash=np.inf, accumulate=True,\\\n",
    "    group_by=np.repeat(np.arange(500), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 ms ± 564 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_signals(\\\n",
    "    big_price_wide, big_entries_wide, big_exits_wide,\\\n",
    "    size=1., init_cash=np.inf, accumulate=True,\\\n",
    "    group_by=np.repeat(np.arange(500), 2), cash_sharing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101 ms ± 191 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# worst case\n",
    "%timeit vbt.Portfolio.from_signals(\\\n",
    "    big_price_wide, big_entries_wide, big_exits_wide,\\\n",
    "    size=np.arange(1000)[::-1], init_cash=np.inf, accumulate=True,\\\n",
    "    group_by=np.repeat(np.arange(500), 2), cash_sharing=True, call_seq=CallSeqType.Auto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98.6 ms ± 848 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_signals(\\\n",
    "    big_price_wide, big_entries_wide, big_exits_wide,\\\n",
    "    size=1., init_cash=np.inf, accumulate=True,\\\n",
    "    group_by=np.full(1000, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96 ms ± 501 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_signals(\\\n",
    "    big_price_wide, big_entries_wide, big_exits_wide,\\\n",
    "    size=1., init_cash=np.inf, accumulate=True,\\\n",
    "    group_by=np.full(1000, 0), cash_sharing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "279 ms ± 1.83 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# worst case\n",
    "%timeit vbt.Portfolio.from_signals(\\\n",
    "    big_price_wide, big_entries_wide, big_exits_wide,\\\n",
    "    size=np.arange(1000)[::-1], init_cash=np.inf, accumulate=True,\\\n",
    "    group_by=np.full(1000, 0), cash_sharing=True, call_seq=CallSeqType.Auto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_per_direction(price=price, entries=entries, exits=exits, **kwargs):\n",
    "    print('Both')\n",
    "    try:\n",
    "        portfolio = vbt.Portfolio.from_signals(price, entries, exits, direction='both', **kwargs)\n",
    "        print(portfolio.orders.records)\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "    print('LongOnly')\n",
    "    try:\n",
    "        portfolio = vbt.Portfolio.from_signals(price, entries, exits, direction='longonly', **kwargs)\n",
    "        print(portfolio.orders.records)\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "    print('ShortOnly')\n",
    "    try:\n",
    "        portfolio = vbt.Portfolio.from_signals(price, entries, exits, direction='shortonly', **kwargs)\n",
    "        print(portfolio.orders.records)\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    3  200.0    4.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    3  100.0    4.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     1\n",
      "1   1    0    3   50.0    4.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    1    0    1.0    1.0   0.0     0\n",
      "1   1    1    3    2.0    4.0   0.0     1\n",
      "2   2    2    0  100.0    1.0   0.0     0\n",
      "3   3    2    3  200.0    4.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    1    0    1.0    1.0   0.0     0\n",
      "1   1    1    3    1.0    4.0   0.0     1\n",
      "2   2    2    0  100.0    1.0   0.0     0\n",
      "3   3    2    3  100.0    4.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    1    0    1.0    1.0   0.0     1\n",
      "1   1    1    3    1.0    4.0   0.0     0\n",
      "2   2    2    0  100.0    1.0   0.0     1\n",
      "3   3    2    3   50.0    4.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=[[0, 1, np.inf]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "SizeType.Percent does not support position reversal using signals\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0  50.0    1.0   0.0     0\n",
      "1   1    0    3  50.0    4.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0  50.0    1.0   0.0     1\n",
      "1   1    0    3  37.5    4.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=0.5, size_type='percent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx    size  price  fees  side\n",
      "0   0    0    0  50.000    1.0   0.0     0\n",
      "1   1    0    1  12.500    2.0   0.0     0\n",
      "2   2    0    3  65.625    4.0   0.0     1\n",
      "3   3    0    4  26.250    5.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx    size  price  fees  side\n",
      "0   0    0    0  50.000    1.0   0.0     0\n",
      "1   1    0    1  12.500    2.0   0.0     0\n",
      "2   2    0    3  31.250    4.0   0.0     1\n",
      "3   3    0    4  15.625    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx    size  price  fees  side\n",
      "0   0    0    0  50.000    1.0   0.0     1\n",
      "1   1    0    1  12.500    2.0   0.0     1\n",
      "2   2    0    3  21.875    4.0   0.0     0\n",
      "3   3    0    4   8.750    5.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=0.5, size_type='percent', accumulate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx        size  price  fees  side\n",
      "0   0    0    0   99.009901   1.01   0.0     0\n",
      "1   1    0    3  198.019802   4.04   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx       size  price  fees  side\n",
      "0   0    0    0  99.009901   1.01   0.0     0\n",
      "1   1    0    3  99.009901   4.04   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx       size  price  fees  side\n",
      "0   0    0    0  99.009901   1.01   0.0     1\n",
      "1   1    0    3  49.504950   4.04   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(price=price * 1.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    3   2.0    4.0   0.0     1\n",
      "2   2    1    0   1.0    1.0   0.1     0\n",
      "3   3    1    3   2.0    4.0   0.8     1\n",
      "4   4    2    0   1.0    1.0   1.0     0\n",
      "5   5    2    3   2.0    4.0   8.0     1\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    3   1.0    4.0   0.0     1\n",
      "2   2    1    0   1.0    1.0   0.1     0\n",
      "3   3    1    3   1.0    4.0   0.4     1\n",
      "4   4    2    0   1.0    1.0   1.0     0\n",
      "5   5    2    3   1.0    4.0   4.0     1\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     1\n",
      "1   1    0    3   1.0    4.0   0.0     0\n",
      "2   2    1    0   1.0    1.0   0.1     1\n",
      "3   3    1    3   1.0    4.0   0.4     0\n",
      "4   4    2    0   1.0    1.0   1.0     1\n",
      "5   5    2    3   1.0    4.0   4.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=1, fees=[[0., 0.1, 1.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    3   2.0    4.0   0.0     1\n",
      "2   2    1    0   1.0    1.0   0.1     0\n",
      "3   3    1    3   2.0    4.0   0.1     1\n",
      "4   4    2    0   1.0    1.0   1.0     0\n",
      "5   5    2    3   2.0    4.0   1.0     1\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    3   1.0    4.0   0.0     1\n",
      "2   2    1    0   1.0    1.0   0.1     0\n",
      "3   3    1    3   1.0    4.0   0.1     1\n",
      "4   4    2    0   1.0    1.0   1.0     0\n",
      "5   5    2    3   1.0    4.0   1.0     1\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     1\n",
      "1   1    0    3   1.0    4.0   0.0     0\n",
      "2   2    1    0   1.0    1.0   0.1     1\n",
      "3   3    1    3   1.0    4.0   0.1     0\n",
      "4   4    2    0   1.0    1.0   1.0     1\n",
      "5   5    2    3   1.0    4.0   1.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=1, fixed_fees=[[0., 0.1, 1.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    3   2.0    4.0   0.0     1\n",
      "2   2    1    0   1.0    1.1   0.0     0\n",
      "3   3    1    3   2.0    3.6   0.0     1\n",
      "4   4    2    0   1.0    2.0   0.0     0\n",
      "5   5    2    3   2.0    0.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    3   1.0    4.0   0.0     1\n",
      "2   2    1    0   1.0    1.1   0.0     0\n",
      "3   3    1    3   1.0    3.6   0.0     1\n",
      "4   4    2    0   1.0    2.0   0.0     0\n",
      "5   5    2    3   1.0    0.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     1\n",
      "1   1    0    3   1.0    4.0   0.0     0\n",
      "2   2    1    0   1.0    0.9   0.0     1\n",
      "3   3    1    3   1.0    4.4   0.0     0\n",
      "4   4    2    0   1.0    0.0   0.0     1\n",
      "5   5    2    3   1.0    8.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=1, slippage=[[0., 0.1, 1.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    3   2.0    4.0   0.0     1\n",
      "2   2    1    0   1.0    1.0   0.0     0\n",
      "3   3    1    3   2.0    4.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    3   1.0    4.0   0.0     1\n",
      "2   2    1    0   1.0    1.0   0.0     0\n",
      "3   3    1    3   1.0    4.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     1\n",
      "1   1    0    3   1.0    4.0   0.0     0\n",
      "2   2    1    0   1.0    1.0   0.0     1\n",
      "3   3    1    3   1.0    4.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=1, min_size=[[0., 1., 2.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   0.5    1.0   0.0     0\n",
      "1   1    0    3   0.5    4.0   0.0     1\n",
      "2   2    0    4   0.5    5.0   0.0     1\n",
      "3   3    1    0   1.0    1.0   0.0     0\n",
      "4   4    1    3   1.0    4.0   0.0     1\n",
      "5   5    1    4   1.0    5.0   0.0     1\n",
      "6   6    2    0   1.0    1.0   0.0     0\n",
      "7   7    2    3   2.0    4.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   0.5    1.0   0.0     0\n",
      "1   1    0    3   0.5    4.0   0.0     1\n",
      "2   2    1    0   1.0    1.0   0.0     0\n",
      "3   3    1    3   1.0    4.0   0.0     1\n",
      "4   4    2    0   1.0    1.0   0.0     0\n",
      "5   5    2    3   1.0    4.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   0.5    1.0   0.0     1\n",
      "1   1    0    3   0.5    4.0   0.0     0\n",
      "2   2    1    0   1.0    1.0   0.0     1\n",
      "3   3    1    3   1.0    4.0   0.0     0\n",
      "4   4    2    0   1.0    1.0   0.0     1\n",
      "5   5    2    3   1.0    4.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=1, max_size=[[0.5, 1., np.inf]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    3   2.0    4.0   0.0     1\n",
      "2   2    1    1   1.0    2.0   0.0     0\n",
      "3   3    1    3   2.0    4.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    3   1.0    4.0   0.0     1\n",
      "2   2    1    1   1.0    2.0   0.0     0\n",
      "3   3    1    3   1.0    4.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     1\n",
      "1   1    0    3   1.0    4.0   0.0     0\n",
      "2   2    1    1   1.0    2.0   0.0     1\n",
      "3   3    1    3   1.0    4.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=1., reject_prob=[[0., 0.5, 1.]], seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    3  200.0    4.0   0.0     1\n",
      "2   2    1    0  100.0    1.0   0.0     0\n",
      "3   3    1    3  100.0    4.0   0.0     1\n",
      "4   4    1    4   80.0    5.0   0.0     1\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0   20.0    5.0   0.0     1\n",
      "1   1    0    3  100.0    2.0   0.0     0\n",
      "2   2    1    0   20.0    5.0   0.0     1\n",
      "3   3    1    3   20.0    2.0   0.0     0\n",
      "4   4    1    4  160.0    1.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_signals(\n",
    "    price, entries, exits, direction='both',\n",
    "    upon_opposite_entry=[['reverse', 'close']]\n",
    ")\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_signals(\n",
    "    pd.Series(price.values[::-1], index=price.index),\n",
    "    pd.Series(entries.values[::-1], index=price.index),\n",
    "    pd.Series(exits.values[::-1], index=price.index), \n",
    "    direction='both',\n",
    "    upon_opposite_entry=[['reverse', 'close']]\n",
    ")\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx    size  price  fees  side\n",
      "0   0    0    0   100.0    1.0   0.0     0\n",
      "1   1    0    3  1100.0    4.0   0.0     1\n",
      "2   2    1    3  1000.0    4.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    3  100.0    4.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx    size  price  fees  side\n",
      "0   0    0    0  1000.0    1.0   0.0     1\n",
      "1   1    0    3   275.0    4.0   0.0     0\n",
      "2   2    1    0  1000.0    1.0   0.0     1\n",
      "Both\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    3  200.0    4.0   0.0     1\n",
      "2   2    1    0  100.0    1.0   0.0     0\n",
      "3   3    1    3  200.0    4.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    3  100.0    4.0   0.0     1\n",
      "2   2    1    0  100.0    1.0   0.0     0\n",
      "3   3    1    3  100.0    4.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     1\n",
      "1   1    0    3   50.0    4.0   0.0     0\n",
      "2   2    1    0  100.0    1.0   0.0     1\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=1000, allow_partial=[[True, False]])\n",
    "test_per_direction(size=np.inf, allow_partial=[[True, False]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx    size  price  fees  side\n",
      "0   0    0    0   100.0    1.0   0.0     0\n",
      "1   1    0    3  1100.0    4.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    3  100.0    4.0   0.0     1\n",
      "ShortOnly\n",
      "Not enough cash to cover fees\n",
      "Both\n",
      "Final size is less than requested\n",
      "LongOnly\n",
      "Final size is less than requested\n",
      "ShortOnly\n",
      "Final size is less than requested\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=1000, allow_partial=True, raise_reject=True)\n",
    "test_per_direction(size=1000, allow_partial=False, raise_reject=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    1   1.0    2.0   0.0     0\n",
      "2   2    0    3   1.0    4.0   0.0     1\n",
      "3   3    0    4   1.0    5.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    0    1   1.0    2.0   0.0     0\n",
      "2   2    0    3   1.0    4.0   0.0     1\n",
      "3   3    0    4   1.0    5.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     1\n",
      "1   1    0    1   1.0    2.0   0.0     1\n",
      "2   2    0    3   1.0    4.0   0.0     0\n",
      "3   3    0    4   1.0    5.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(size=1, accumulate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    3  200.0    4.0   0.0     1\n",
      "   id  group  col  idx   cash  position  debt  free_cash  val_price  value  \\\n",
      "0   0      0    0    0  100.0       0.0   0.0      100.0        1.0  100.0   \n",
      "1   1      0    0    3    0.0     100.0   0.0        0.0        4.0  400.0   \n",
      "\n",
      "   ...  new_free_cash  new_val_price  new_value  res_size  res_price  \\\n",
      "0  ...            0.0            1.0      100.0     100.0        1.0   \n",
      "1  ...            0.0            4.0      400.0     200.0        4.0   \n",
      "\n",
      "   res_fees  res_side  res_status  res_status_info  order_id  \n",
      "0       0.0         0           0               -1         0  \n",
      "1       0.0         1           0               -1         1  \n",
      "\n",
      "[2 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_signals(price, entries, exits, log=True, direction='both')\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.logs.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    1   1.0    2.0   0.0     0\n",
      "1   1    1    1   1.0    2.0   0.0     0\n",
      "2   2    2    1   1.0    2.0   0.0     0\n",
      "3   3    3    1   1.0    2.0   0.0     0\n",
      "4   4    4    1   1.0    2.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    1   1.0    2.0   0.0     0\n",
      "1   1    1    0   1.0    1.0   0.0     0\n",
      "2   2    2    1   1.0    2.0   0.0     0\n",
      "3   3    2    2   1.0    3.0   0.0     1\n",
      "4   4    3    1   1.0    2.0   0.0     0\n",
      "5   5    3    2   1.0    3.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    1   1.0    2.0   0.0     1\n",
      "1   1    1    0   1.0    1.0   0.0     1\n",
      "2   2    2    1   1.0    2.0   0.0     1\n",
      "3   3    2    2   1.0    3.0   0.0     0\n",
      "4   4    3    1   1.0    2.0   0.0     1\n",
      "5   5    3    2   1.0    3.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(price=price.iloc[:3], entries=pd.DataFrame([\n",
    "    [True, True, True, True, True],\n",
    "    [True, True, True, True, False],\n",
    "    [True, True, True, True, True]\n",
    "]), exits=pd.DataFrame([\n",
    "    [True, True, True, True, True],\n",
    "    [False, False, False, False, True],\n",
    "    [True, True, True, True, True]\n",
    "]), size=1., upon_long_conflict=[[\n",
    "    'ignore', \n",
    "    'entry', \n",
    "    'exit', \n",
    "    'opposite',\n",
    "    'opposite'\n",
    "]], upon_short_conflict=[[\n",
    "    'ignore', \n",
    "    'entry', \n",
    "    'exit', \n",
    "    'opposite',\n",
    "    'opposite'\n",
    "]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    1   1.0    2.0   0.0     0\n",
      "1   1    1    0   1.0    1.0   0.0     0\n",
      "2   2    2    0   1.0    1.0   0.0     1\n",
      "3   3    2    1   2.0    2.0   0.0     0\n",
      "4   4    2    2   2.0    3.0   0.0     1\n",
      "5   5    3    1   1.0    2.0   0.0     0\n",
      "6   6    3    2   2.0    3.0   0.0     1\n",
      "7   7    4    1   1.0    2.0   0.0     1\n",
      "8   8    4    2   2.0    3.0   0.0     0\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    1   1.0    2.0   0.0     0\n",
      "1   1    1    1   1.0    2.0   0.0     0\n",
      "2   2    2    1   1.0    2.0   0.0     0\n",
      "3   3    3    1   1.0    2.0   0.0     0\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    1   1.0    2.0   0.0     1\n",
      "1   1    1    1   1.0    2.0   0.0     1\n",
      "2   2    2    1   1.0    2.0   0.0     1\n",
      "3   3    3    1   1.0    2.0   0.0     1\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(price=price.iloc[:3], entries=pd.DataFrame([\n",
    "    [True, True, True, True, True],\n",
    "    [True, True, True, True, False],\n",
    "    [True, True, True, True, True]\n",
    "]), exits=pd.DataFrame([\n",
    "    [True, True, True, True, True],\n",
    "    [False, False, False, False, True],\n",
    "    [True, True, True, True, True]\n",
    "]), size=1., upon_dir_conflict=[[\n",
    "    'ignore', \n",
    "    'long', \n",
    "    'short', \n",
    "    'opposite',\n",
    "    'opposite'\n",
    "]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    3   1.0    4.0   0.0     1\n",
      "1   1    1    0   1.0    1.0   0.0     0\n",
      "2   2    1    3   2.0    4.0   0.0     1\n",
      "3   3    2    0   1.0    1.0   0.0     0\n",
      "4   4    2    3   2.0    4.0   0.0     1\n",
      "LongOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    1    0   1.0    1.0   0.0     0\n",
      "1   1    1    3   1.0    4.0   0.0     1\n",
      "2   2    2    0   1.0    1.0   0.0     0\n",
      "3   3    2    3   1.0    4.0   0.0     1\n",
      "ShortOnly\n",
      "   id  col  idx  size  price  fees  side\n",
      "0   0    0    0  1.00    1.0   0.0     1\n",
      "1   1    0    3  0.25    4.0   0.0     0\n",
      "2   2    1    0  1.00    1.0   0.0     1\n",
      "3   3    1    3  0.50    4.0   0.0     0\n",
      "4   4    2    0  1.00    1.0   0.0     1\n",
      "5   5    2    3  1.00    4.0   0.0     0\n",
      "Both\n",
      "Attempt to go in long direction infinitely\n",
      "LongOnly\n",
      "Attempt to go in long direction infinitely\n",
      "ShortOnly\n",
      "Attempt to go in short direction infinitely\n"
     ]
    }
   ],
   "source": [
    "test_per_direction(price=price_wide, size=1., init_cash=[0., 1., 100.])\n",
    "test_per_direction(init_cash=np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    3  200.0    4.0   0.0     1\n",
      "2   2    1    0  100.0    1.0   0.0     0\n",
      "3   3    1    3  200.0    4.0   0.0     1\n",
      "4   4    2    0  100.0    1.0   0.0     0\n",
      "5   5    2    3  200.0    4.0   0.0     1\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_signals(\n",
    "    price_wide, entries, exits, \n",
    "    group_by=np.array([0, 0, 1]), direction='both')\n",
    "\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  col  idx   size  price  fees  side\n",
      "0   0    0    0  100.0    1.0   0.0     0\n",
      "1   1    0    3  200.0    4.0   0.0     1\n",
      "2   2    2    0  100.0    1.0   0.0     0\n",
      "3   3    2    3  200.0    4.0   0.0     1\n",
      "            a  b  c\n",
      "2020-01-01  0  1  0\n",
      "2020-01-02  0  1  0\n",
      "2020-01-03  0  1  0\n",
      "2020-01-04  0  1  0\n",
      "2020-01-05  0  1  0\n",
      "Cannot modify grouping globally when cash_sharing=True\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_signals(\n",
    "    price_wide, entries, exits, \n",
    "    group_by=np.array([0, 0, 1]), cash_sharing=True, direction='both')\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.call_seq)\n",
    "try:\n",
    "    print(portfolio.regroup(group_by=False))\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  col  idx   size  price  fees  side\n",
      "0   0    1    0  100.0    1.0   0.0     0\n",
      "1   1    1    3  200.0    4.0   0.0     1\n",
      "2   2    2    0  100.0    1.0   0.0     0\n",
      "3   3    2    3  200.0    4.0   0.0     1\n",
      "            a  b  c\n",
      "2020-01-01  1  0  0\n",
      "2020-01-02  1  0  0\n",
      "2020-01-03  1  0  0\n",
      "2020-01-04  1  0  0\n",
      "2020-01-05  1  0  0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_signals(\n",
    "    price_wide, entries, exits, \n",
    "    group_by=np.array([0, 0, 1]), cash_sharing=True, direction='both', call_seq='reversed')\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.call_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  col  idx   size  price  fees  side\n",
      "0   0    1    0  100.0    1.0   0.0     0\n",
      "1   1    1    3  200.0    4.0   0.0     1\n",
      "2   2    2    0  100.0    1.0   0.0     0\n",
      "3   3    2    3  200.0    4.0   0.0     1\n",
      "            a  b  c\n",
      "2020-01-01  1  0  0\n",
      "2020-01-02  1  0  0\n",
      "2020-01-03  1  0  0\n",
      "2020-01-04  1  0  0\n",
      "2020-01-05  0  1  0\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    1    0  100.0    1.0   0.0     0\n",
      "1   1    1    3  200.0    4.0   0.0     1\n",
      "2   2    2    0  100.0    1.0   0.0     0\n",
      "3   3    2    3  200.0    4.0   0.0     1\n",
      "            a  b  c\n",
      "2020-01-01  1  0  0\n",
      "2020-01-02  0  1  0\n",
      "2020-01-03  1  0  0\n",
      "2020-01-04  1  0  0\n",
      "2020-01-05  1  0  0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_signals(\n",
    "    price_wide, entries, exits, \n",
    "    group_by=np.array([0, 0, 1]), cash_sharing=True, direction='both', call_seq='random')\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.call_seq)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_signals(\n",
    "    price_wide, entries, exits, \n",
    "    group_by=np.array([0, 0, 1]), cash_sharing=True, direction='both', call_seq='random', seed=42)\n",
    "\n",
    "print(portfolio.orders.records)\n",
    "print(portfolio.call_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    2    0  100.0    1.0   0.0     0\n",
      "1   1    2    1  200.0    1.0   0.0     1\n",
      "2   2    1    1  200.0    1.0   0.0     0\n",
      "3   3    1    2  200.0    1.0   0.0     1\n",
      "4   4    0    2  200.0    1.0   0.0     0\n",
      "5   5    0    3  200.0    1.0   0.0     1\n",
      "6   6    2    3  200.0    1.0   0.0     0\n",
      "7   7    2    4  200.0    1.0   0.0     1\n",
      "8   8    1    4  200.0    1.0   0.0     0\n",
      "   0  1  2\n",
      "0  0  1  2\n",
      "1  2  0  1\n",
      "2  1  2  0\n",
      "3  0  1  2\n",
      "4  2  0  1\n",
      "       0      1      2\n",
      "0    0.0    0.0  100.0\n",
      "1    0.0  200.0 -100.0\n",
      "2  200.0    0.0 -100.0\n",
      "3    0.0    0.0  100.0\n",
      "4    0.0  200.0 -100.0\n",
      "0    100.0\n",
      "1    100.0\n",
      "2    100.0\n",
      "3    100.0\n",
      "4    100.0\n",
      "dtype: float64\n",
      "LongOnly\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    2    0  100.0    1.0   0.0     0\n",
      "1   1    2    1  100.0    1.0   0.0     1\n",
      "2   2    1    1  100.0    1.0   0.0     0\n",
      "3   3    1    2  100.0    1.0   0.0     1\n",
      "4   4    0    2  100.0    1.0   0.0     0\n",
      "5   5    0    3  100.0    1.0   0.0     1\n",
      "6   6    2    3  100.0    1.0   0.0     0\n",
      "7   7    2    4  100.0    1.0   0.0     1\n",
      "8   8    1    4  100.0    1.0   0.0     0\n",
      "   0  1  2\n",
      "0  0  1  2\n",
      "1  2  0  1\n",
      "2  1  2  0\n",
      "3  0  1  2\n",
      "4  2  0  1\n",
      "       0      1      2\n",
      "0    0.0    0.0  100.0\n",
      "1    0.0  100.0    0.0\n",
      "2  100.0    0.0    0.0\n",
      "3    0.0    0.0  100.0\n",
      "4    0.0  100.0    0.0\n",
      "0    100.0\n",
      "1    100.0\n",
      "2    100.0\n",
      "3    100.0\n",
      "4    100.0\n",
      "dtype: float64\n",
      "ShortOnly\n",
      "   id  col  idx   size  price  fees  side\n",
      "0   0    2    0  100.0    1.0   0.0     1\n",
      "1   1    2    1  100.0    1.0   0.0     0\n",
      "2   2    0    2  100.0    1.0   0.0     1\n",
      "3   3    0    3  100.0    1.0   0.0     0\n",
      "4   4    1    4  100.0    1.0   0.0     1\n",
      "   0  1  2\n",
      "0  2  0  1\n",
      "1  1  0  2\n",
      "2  0  1  2\n",
      "3  2  1  0\n",
      "4  1  0  2\n",
      "       0      1      2\n",
      "0    0.0    0.0 -100.0\n",
      "1    0.0    0.0    0.0\n",
      "2 -100.0    0.0    0.0\n",
      "3    0.0    0.0    0.0\n",
      "4    0.0 -100.0    0.0\n",
      "0    100.0\n",
      "1    100.0\n",
      "2    100.0\n",
      "3    100.0\n",
      "4    100.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for direction in ('Both', 'LongOnly', 'ShortOnly'):\n",
    "    print(direction)\n",
    "    portfolio = vbt.Portfolio.from_signals(1., pd.DataFrame([\n",
    "        [False, False, True],\n",
    "        [False, True, False],\n",
    "        [True, False, False],\n",
    "        [False, False, True],\n",
    "        [False, True, False],\n",
    "    ]), pd.DataFrame([\n",
    "        [False, False, False],\n",
    "        [False, False, True],\n",
    "        [False, True, False],\n",
    "        [True, False, False],\n",
    "        [False, False, True],\n",
    "    ]), group_by=np.array([0, 0, 0]), \n",
    "        cash_sharing=True, call_seq='auto', direction=direction)\n",
    "\n",
    "    print(portfolio.orders.records)\n",
    "    print(portfolio.call_seq)\n",
    "    print(portfolio.assets())\n",
    "    print(portfolio.value())\n",
    "    # notice how shorting with np.inf only accounts for current cash, not for other assets!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## from_order_func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vectorbt.portfolio.nb import order_nb, order_nothing_nb\n",
    "\n",
    "@njit\n",
    "def order_func_nb(c, size):\n",
    "    return order_nb(size if c.i % 2 == 0 else -size)\n",
    "\n",
    "@njit\n",
    "def flex_order_func_nb(c, size):\n",
    "    if c.call_idx < c.group_len:\n",
    "        return c.from_col + c.call_idx, order_nb(size if c.i % 2 == 0 else -size)\n",
    "    return -1, order_nothing_nb()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.92 ms ± 156 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "2.02 ms ± 189 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1.85 ms ± 140 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1.85 ms ± 145 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price, order_func_nb, 1., init_cash=np.inf)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price, order_func_nb, 1., init_cash=np.inf, row_wise=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price, flex_order_func_nb, 1., init_cash=np.inf, flexible=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price, flex_order_func_nb, 1., init_cash=np.inf, row_wise=True, flexible=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95 ms ± 181 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "101 ms ± 2.48 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "90.9 ms ± 489 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "89.1 ms ± 1.69 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., init_cash=np.inf)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., init_cash=np.inf, row_wise=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., init_cash=np.inf, flexible=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., init_cash=np.inf, row_wise=True, flexible=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76.4 ms ± 217 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "84.2 ms ± 284 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "71.5 ms ± 1.39 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "71.1 ms ± 101 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., init_cash=np.inf, fill_pos_record=False)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., init_cash=np.inf, row_wise=True, fill_pos_record=False)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., init_cash=np.inf, fill_pos_record=False, flexible=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., init_cash=np.inf, row_wise=True, fill_pos_record=False, flexible=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89.1 ms ± 851 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "93.5 ms ± 692 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "86.2 ms ± 287 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "86.3 ms ± 131 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., \\\n",
    "    group_by=np.repeat(np.arange(500), 2), init_cash=np.inf)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., \\\n",
    "    group_by=np.repeat(np.arange(500), 2), init_cash=np.inf, row_wise=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., \\\n",
    "    group_by=np.repeat(np.arange(500), 2), init_cash=np.inf, flexible=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., \\\n",
    "    group_by=np.repeat(np.arange(500), 2), init_cash=np.inf, row_wise=True, flexible=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88.7 ms ± 763 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "93 ms ± 686 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "85.4 ms ± 39.1 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "85.7 ms ± 140 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., \\\n",
    "    group_by=np.repeat(np.arange(500), 2), cash_sharing=True, init_cash=np.inf)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., \\\n",
    "    group_by=np.repeat(np.arange(500), 2), cash_sharing=True, init_cash=np.inf, row_wise=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., \\\n",
    "    group_by=np.repeat(np.arange(500), 2), cash_sharing=True, init_cash=np.inf, flexible=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., \\\n",
    "    group_by=np.repeat(np.arange(500), 2), cash_sharing=True, init_cash=np.inf, row_wise=True, flexible=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87 ms ± 487 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "86.2 ms ± 369 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "83.4 ms ± 151 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "83.3 ms ± 153 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0))\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0), row_wise=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0), flexible=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0), row_wise=True, flexible=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83.6 ms ± 622 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "82.7 ms ± 192 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "82.3 ms ± 927 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "82 ms ± 680 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0), cash_sharing=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, order_func_nb, 1., init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0), cash_sharing=True, row_wise=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0), cash_sharing=True, flexible=True)\n",
    "%timeit vbt.Portfolio.from_order_func(\\\n",
    "    big_price_wide, flex_order_func_nb, 1., init_cash=np.inf, \\\n",
    "    group_by=np.full(1000, 0), cash_sharing=True, row_wise=True, flexible=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_call_seq(call_seq, **kwargs):\n",
    "    return vbt.Portfolio.from_order_func(\n",
    "        price_wide, \n",
    "        order_func_nb, \n",
    "        1.,\n",
    "        group_by=np.array([0, 0, 0]),\n",
    "        call_seq=call_seq, **kwargs\n",
    "    ).call_seq\n",
    "\n",
    "def big_test_call_seq(call_seq, **kwargs):\n",
    "    return vbt.Portfolio.from_order_func(\n",
    "        big_price_wide, \n",
    "        order_func_nb, \n",
    "        1.,\n",
    "        group_by=np.repeat(np.arange(500), 2),\n",
    "        call_seq=call_seq, **kwargs\n",
    "    ).call_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            a  b  c\n",
      "2020-01-01  0  1  2\n",
      "2020-01-02  0  1  2\n",
      "2020-01-03  0  1  2\n",
      "2020-01-04  0  1  2\n",
      "2020-01-05  0  1  2\n",
      "46.2 ms ± 838 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "            a  b  c\n",
      "2020-01-01  2  1  0\n",
      "2020-01-02  2  1  0\n",
      "2020-01-03  2  1  0\n",
      "2020-01-04  2  1  0\n",
      "2020-01-05  2  1  0\n",
      "46.4 ms ± 683 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "            a  b  c\n",
      "2020-01-01  0  1  2\n",
      "2020-01-02  2  1  0\n",
      "2020-01-03  1  0  2\n",
      "2020-01-04  2  0  1\n",
      "2020-01-05  2  1  0\n",
      "55.1 ms ± 849 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "print(test_call_seq(CallSeqType.Default))\n",
    "%timeit big_test_call_seq(CallSeqType.Default)\n",
    "\n",
    "print(test_call_seq(CallSeqType.Reversed))\n",
    "%timeit big_test_call_seq(CallSeqType.Reversed)\n",
    "\n",
    "print(test_call_seq(CallSeqType.Random))\n",
    "%timeit big_test_call_seq(CallSeqType.Random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def pre_segment_func1_nb(c, *args):\n",
    "    c.call_seq_now[:] = np.arange(c.to_col - c.from_col - 1, -1, -1)\n",
    "    return ()\n",
    "\n",
    "@njit\n",
    "def pre_segment_func2_nb(c, *args):\n",
    "    c.call_seq_now[:] = np.copy(c.call_seq_now[::-1])\n",
    "    return ()\n",
    "\n",
    "@njit\n",
    "def pre_segment_func3_nb(c, *args):\n",
    "    call_seq_now = c.call_seq_now\n",
    "    n_cols = c.to_col - c.from_col\n",
    "    for k in range(n_cols):\n",
    "        call_seq_now[k] = n_cols - k - 1\n",
    "    return ()\n",
    "\n",
    "@njit\n",
    "def pre_segment_func4_nb(c, *args):\n",
    "    np.random.shuffle(c.call_seq_now)\n",
    "    return ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            a  b  c\n",
      "2020-01-01  2  1  0\n",
      "2020-01-02  2  1  0\n",
      "2020-01-03  2  1  0\n",
      "2020-01-04  2  1  0\n",
      "2020-01-05  2  1  0\n",
      "262 ms ± 109 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "206 ms ± 2.16 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "            a  b  c\n",
      "2020-01-01  2  1  0\n",
      "2020-01-02  2  1  0\n",
      "2020-01-03  2  1  0\n",
      "2020-01-04  2  1  0\n",
      "2020-01-05  2  1  0\n",
      "209 ms ± 1.67 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "211 ms ± 1.97 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "            a  b  c\n",
      "2020-01-01  2  1  0\n",
      "2020-01-02  2  1  0\n",
      "2020-01-03  2  1  0\n",
      "2020-01-04  2  1  0\n",
      "2020-01-05  2  1  0\n",
      "94.2 ms ± 1.26 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "99.2 ms ± 394 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "            a  b  c\n",
      "2020-01-01  1  2  0\n",
      "2020-01-02  0  1  2\n",
      "2020-01-03  1  0  2\n",
      "2020-01-04  1  0  2\n",
      "2020-01-05  2  1  0\n",
      "148 ms ± 2.78 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "148 ms ± 3.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "print(test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func1_nb))\n",
    "%timeit big_test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func1_nb)\n",
    "%timeit big_test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func1_nb, row_wise=True)\n",
    "\n",
    "print(test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func2_nb))\n",
    "%timeit big_test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func2_nb)\n",
    "%timeit big_test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func2_nb, row_wise=True)\n",
    "\n",
    "print(test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func3_nb))\n",
    "%timeit big_test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func3_nb)\n",
    "%timeit big_test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func3_nb, row_wise=True)\n",
    "\n",
    "print(test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func4_nb))\n",
    "%timeit big_test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func4_nb)  # in-place fastest\n",
    "%timeit big_test_call_seq(CallSeqType.Default, pre_segment_func_nb=pre_segment_func4_nb, row_wise=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([0], dtype='int64')\n",
      "1\n",
      "Index([0], dtype='int64', name='iteration_idx')\n",
      "2\n",
      "Index(['first'], dtype='object', name='custom')\n",
      "2\n",
      "Index([0, 1, 2], dtype='int64', name='iteration_idx')\n",
      "2\n",
      "Index(['first', 'second', 'third'], dtype='object', name='custom')\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price, order_func_nb, np.inf,\n",
    "    target_shape=(5,))\n",
    "print(portfolio.wrapper.columns)\n",
    "print(portfolio.wrapper.ndim)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price, order_func_nb, np.inf,\n",
    "    target_shape=(5, 1))\n",
    "print(portfolio.wrapper.columns)\n",
    "print(portfolio.wrapper.ndim)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price, order_func_nb, np.inf,\n",
    "    target_shape=(5, 1), keys=pd.Index(['first'], name='custom'))\n",
    "print(portfolio.wrapper.columns)\n",
    "print(portfolio.wrapper.ndim)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price, order_func_nb, np.inf,\n",
    "    target_shape=(5, 3))\n",
    "print(portfolio.wrapper.columns)\n",
    "print(portfolio.wrapper.ndim)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price, order_func_nb, np.inf,\n",
    "    target_shape=(5, 3), keys=pd.Index(['first', 'second', 'third'], name='custom'))\n",
    "print(portfolio.wrapper.columns)\n",
    "print(portfolio.wrapper.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col        size  price  fees  side\n",
      "0   0    0    0  100.000000    1.0   0.0     0\n",
      "1   1    1    0  200.000000    2.0   0.0     1\n",
      "2   2    2    0  133.333333    3.0   0.0     0\n",
      "3   3    3    0   66.666667    4.0   0.0     1\n",
      "4   4    4    0   53.333333    5.0   0.0     0\n",
      "   id  idx  col        size  price  fees  side\n",
      "0   0    0    0  100.000000    1.0   0.0     0\n",
      "1   1    1    0  200.000000    2.0   0.0     1\n",
      "2   2    2    0  133.333333    3.0   0.0     0\n",
      "3   3    3    0   66.666667    4.0   0.0     1\n",
      "4   4    4    0   53.333333    5.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_order_func(price, order_func_nb, np.inf)\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(price, order_func_nb, np.inf, row_wise=True)\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    id  idx  col        size  price  fees  side\n",
      "0    0    0    0  100.000000    1.0   0.0     0\n",
      "1    1    1    0  200.000000    2.0   0.0     1\n",
      "2    2    2    0  133.333333    3.0   0.0     0\n",
      "3    3    3    0   66.666667    4.0   0.0     1\n",
      "4    4    4    0   53.333333    5.0   0.0     0\n",
      "5    5    0    1  100.000000    1.0   0.0     0\n",
      "6    6    1    1  200.000000    2.0   0.0     1\n",
      "7    7    2    1  133.333333    3.0   0.0     0\n",
      "8    8    3    1   66.666667    4.0   0.0     1\n",
      "9    9    4    1   53.333333    5.0   0.0     0\n",
      "10  10    0    2  100.000000    1.0   0.0     0\n",
      "11  11    1    2  200.000000    2.0   0.0     1\n",
      "12  12    2    2  133.333333    3.0   0.0     0\n",
      "13  13    3    2   66.666667    4.0   0.0     1\n",
      "14  14    4    2   53.333333    5.0   0.0     0\n",
      "    id  idx  col        size  price  fees  side\n",
      "0    0    0    0  100.000000    1.0   0.0     0\n",
      "1    1    0    1  100.000000    1.0   0.0     0\n",
      "2    2    0    2  100.000000    1.0   0.0     0\n",
      "3    3    1    0  200.000000    2.0   0.0     1\n",
      "4    4    1    1  200.000000    2.0   0.0     1\n",
      "5    5    1    2  200.000000    2.0   0.0     1\n",
      "6    6    2    0  133.333333    3.0   0.0     0\n",
      "7    7    2    1  133.333333    3.0   0.0     0\n",
      "8    8    2    2  133.333333    3.0   0.0     0\n",
      "9    9    3    0   66.666667    4.0   0.0     1\n",
      "10  10    3    1   66.666667    4.0   0.0     1\n",
      "11  11    3    2   66.666667    4.0   0.0     1\n",
      "12  12    4    0   53.333333    5.0   0.0     0\n",
      "13  13    4    1   53.333333    5.0   0.0     0\n",
      "14  14    4    2   53.333333    5.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_order_func(price_wide, order_func_nb, np.inf)\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(price_wide, order_func_nb, np.inf, row_wise=True)\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    id  idx  col        size  price  fees  side\n",
      "0    0    0    0  100.000000    1.0   0.0     0\n",
      "1    1    0    1  100.000000    1.0   0.0     0\n",
      "2    2    1    0  200.000000    2.0   0.0     1\n",
      "3    3    1    1  200.000000    2.0   0.0     1\n",
      "4    4    2    0  133.333333    3.0   0.0     0\n",
      "5    5    2    1  133.333333    3.0   0.0     0\n",
      "6    6    3    0   66.666667    4.0   0.0     1\n",
      "7    7    3    1   66.666667    4.0   0.0     1\n",
      "8    8    4    0   53.333333    5.0   0.0     0\n",
      "9    9    4    1   53.333333    5.0   0.0     0\n",
      "10  10    0    2  100.000000    1.0   0.0     0\n",
      "11  11    1    2  200.000000    2.0   0.0     1\n",
      "12  12    2    2  133.333333    3.0   0.0     0\n",
      "13  13    3    2   66.666667    4.0   0.0     1\n",
      "14  14    4    2   53.333333    5.0   0.0     0\n",
      "    id  idx  col        size  price  fees  side\n",
      "0    0    0    0  100.000000    1.0   0.0     0\n",
      "1    1    0    1  100.000000    1.0   0.0     0\n",
      "2    2    0    2  100.000000    1.0   0.0     0\n",
      "3    3    1    0  200.000000    2.0   0.0     1\n",
      "4    4    1    1  200.000000    2.0   0.0     1\n",
      "5    5    1    2  200.000000    2.0   0.0     1\n",
      "6    6    2    0  133.333333    3.0   0.0     0\n",
      "7    7    2    1  133.333333    3.0   0.0     0\n",
      "8    8    2    2  133.333333    3.0   0.0     0\n",
      "9    9    3    0   66.666667    4.0   0.0     1\n",
      "10  10    3    1   66.666667    4.0   0.0     1\n",
      "11  11    3    2   66.666667    4.0   0.0     1\n",
      "12  12    4    0   53.333333    5.0   0.0     0\n",
      "13  13    4    1   53.333333    5.0   0.0     0\n",
      "14  14    4    2   53.333333    5.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price_wide, order_func_nb, np.inf,\n",
    "    group_by=np.array([0, 0, 1]))\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price_wide, order_func_nb, np.inf,\n",
    "    group_by=np.array([0, 0, 1]), row_wise=True)\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col        size  price  fees  side\n",
      "0   0    0    0  100.000000    1.0   0.0     0\n",
      "1   1    1    0  200.000000    2.0   0.0     1\n",
      "2   2    2    0  133.333333    3.0   0.0     0\n",
      "3   3    3    0   66.666667    4.0   0.0     1\n",
      "4   4    4    0   53.333333    5.0   0.0     0\n",
      "5   5    0    2  100.000000    1.0   0.0     0\n",
      "6   6    1    2  200.000000    2.0   0.0     1\n",
      "7   7    2    2  133.333333    3.0   0.0     0\n",
      "8   8    3    2   66.666667    4.0   0.0     1\n",
      "9   9    4    2   53.333333    5.0   0.0     0\n",
      "   id  idx  col        size  price  fees  side\n",
      "0   0    0    0  100.000000    1.0   0.0     0\n",
      "1   1    0    2  100.000000    1.0   0.0     0\n",
      "2   2    1    0  200.000000    2.0   0.0     1\n",
      "3   3    1    2  200.000000    2.0   0.0     1\n",
      "4   4    2    0  133.333333    3.0   0.0     0\n",
      "5   5    2    2  133.333333    3.0   0.0     0\n",
      "6   6    3    0   66.666667    4.0   0.0     1\n",
      "7   7    3    2   66.666667    4.0   0.0     1\n",
      "8   8    4    0   53.333333    5.0   0.0     0\n",
      "9   9    4    2   53.333333    5.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price_wide, order_func_nb, np.inf,\n",
    "    group_by=np.array([0, 0, 1]), cash_sharing=True)\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price_wide, order_func_nb, np.inf,\n",
    "    group_by=np.array([0, 0, 1]), cash_sharing=True, row_wise=True)\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.] 25.0\n",
      "[2.] -33.33333333333333\n",
      "[3.] -12.5\n",
      "[4.] -6.666666666666668\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  50.000000    2.0   0.0     0\n",
      "1   1    1    0  25.000000    3.0   0.0     1\n",
      "2   2    2    0   8.333333    4.0   0.0     1\n",
      "3   3    3    0   4.166667    5.0   0.0     1\n",
      "[1.] 25.0\n",
      "[2.] -33.33333333333333\n",
      "[3.] -12.5\n",
      "[4.] -6.666666666666668\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  50.000000    2.0   0.0     0\n",
      "1   1    1    0  25.000000    3.0   0.0     1\n",
      "2   2    2    0   8.333333    4.0   0.0     1\n",
      "3   3    3    0   4.166667    5.0   0.0     1\n"
     ]
    }
   ],
   "source": [
    "@njit\n",
    "def target_val_pre_segment_func_nb(c, val_price):\n",
    "    c.last_val_price[c.from_col:c.to_col] = val_price[c.i]\n",
    "    return ()\n",
    "\n",
    "@njit\n",
    "def target_val_order_func_nb(c):\n",
    "    print(c.last_val_price[c.from_col:c.to_col], 50. / c.close[c.i, c.col] - c.position_now)\n",
    "    return order_nb(size=50., size_type=SizeType.TargetValue, price=c.close[c.i, c.col])\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price.iloc[1:], target_val_order_func_nb, \n",
    "    pre_segment_func_nb=target_val_pre_segment_func_nb,\n",
    "    pre_segment_args=(price.values[:-1],)  # lagged valuation price\n",
    ")\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price.iloc[1:], target_val_order_func_nb, \n",
    "    pre_segment_func_nb=target_val_pre_segment_func_nb,\n",
    "    pre_segment_args=(price.values[:-1],), row_wise=True\n",
    ")\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.] 25.0\n",
      "[3.] -8.333333333333332\n",
      "[4.] -4.166666666666668\n",
      "[5.] -2.5\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  25.000000    2.0   0.0     0\n",
      "1   1    1    0   8.333333    3.0   0.0     1\n",
      "2   2    2    0   4.166667    4.0   0.0     1\n",
      "3   3    3    0   2.500000    5.0   0.0     1\n",
      "[2.] 25.0\n",
      "[3.] -8.333333333333332\n",
      "[4.] -4.166666666666668\n",
      "[5.] -2.5\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  25.000000    2.0   0.0     0\n",
      "1   1    1    0   8.333333    3.0   0.0     1\n",
      "2   2    2    0   4.166667    4.0   0.0     1\n",
      "3   3    3    0   2.500000    5.0   0.0     1\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price.iloc[1:], target_val_order_func_nb, \n",
    "    pre_segment_func_nb=target_val_pre_segment_func_nb,\n",
    "    pre_segment_args=(price.values[1:],)  # current order price\n",
    ")\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price.iloc[1:], target_val_order_func_nb, \n",
    "    pre_segment_func_nb=target_val_pre_segment_func_nb,\n",
    "    pre_segment_args=(price.values[1:],), row_wise=True\n",
    ")\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.] 25.0\n",
      "[2.] -33.33333333333333\n",
      "[3.] -6.25\n",
      "[4.] -7.5\n",
      "   id  idx  col    size  price  fees  side\n",
      "0   0    0    0  50.000    2.0   0.0     0\n",
      "1   1    1    0  25.000    3.0   0.0     1\n",
      "2   2    3    0   3.125    5.0   0.0     1\n",
      "[1.] 25.0\n",
      "[2.] -33.33333333333333\n",
      "[3.] -6.25\n",
      "[4.] -7.5\n",
      "   id  idx  col    size  price  fees  side\n",
      "0   0    0    0  50.000    2.0   0.0     0\n",
      "1   1    1    0  25.000    3.0   0.0     1\n",
      "2   2    3    0   3.125    5.0   0.0     1\n"
     ]
    }
   ],
   "source": [
    "@njit\n",
    "def target_pct_pre_segment_func_nb(c, val_price):\n",
    "    c.last_val_price[c.from_col:c.to_col] = val_price[c.i]\n",
    "    return ()\n",
    "\n",
    "@njit\n",
    "def target_pct_order_func_nb(c):\n",
    "    print(c.last_val_price[c.from_col:c.to_col], 0.5 * c.value_now / c.close[c.i, c.col] - c.position_now)\n",
    "    return order_nb(size=0.5, size_type=SizeType.TargetPercent, price=c.close[c.i, c.col])\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price.iloc[1:], target_pct_order_func_nb, \n",
    "    pre_segment_func_nb=target_pct_pre_segment_func_nb,\n",
    "    pre_segment_args=(price.values[:-1],)  # lagged valuation price\n",
    ")\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price.iloc[1:], target_pct_order_func_nb, \n",
    "    pre_segment_func_nb=target_pct_pre_segment_func_nb,\n",
    "    pre_segment_args=(price.values[:-1],), row_wise=True\n",
    ")\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.] 25.0\n",
      "[3.] -4.166666666666668\n",
      "[4.] -2.604166666666668\n",
      "[5.] -1.8229166666666643\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  25.000000    2.0   0.0     0\n",
      "1   1    1    0   4.166667    3.0   0.0     1\n",
      "2   2    2    0   2.604167    4.0   0.0     1\n",
      "3   3    3    0   1.822917    5.0   0.0     1\n",
      "[2.] 25.0\n",
      "[3.] -4.166666666666668\n",
      "[4.] -2.604166666666668\n",
      "[5.] -1.8229166666666643\n",
      "   id  idx  col       size  price  fees  side\n",
      "0   0    0    0  25.000000    2.0   0.0     0\n",
      "1   1    1    0   4.166667    3.0   0.0     1\n",
      "2   2    2    0   2.604167    4.0   0.0     1\n",
      "3   3    3    0   1.822917    5.0   0.0     1\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price.iloc[1:], target_pct_order_func_nb, \n",
    "    pre_segment_func_nb=target_pct_pre_segment_func_nb,\n",
    "    pre_segment_args=(price.values[1:],)  # current order price\n",
    ")\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price.iloc[1:], target_pct_order_func_nb, \n",
    "    pre_segment_func_nb=target_pct_pre_segment_func_nb,\n",
    "    pre_segment_args=(price.values[1:],), row_wise=True\n",
    ")\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    2    0   1.0    3.0   0.0     0\n",
      "2   2    4    0   1.0    5.0   0.0     0\n",
      "   id  idx  col  size  price  fees  side\n",
      "0   0    0    0   1.0    1.0   0.0     0\n",
      "1   1    2    0   1.0    3.0   0.0     0\n",
      "2   2    4    0   1.0    5.0   0.0     0\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price, order_func_nb, 1.,\n",
    "    segment_mask=pd.Series([True, False, True, False, True]))\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price, order_func_nb, 1.,\n",
    "    segment_mask=pd.Series([True, False, True, False, True]), row_wise=True)\n",
    "print(portfolio.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing simulation\n",
      "\tpreparing group 0\n",
      "\t\tpreparing segment 0 (row)\n",
      "\t\t\tprocessing order 0 at column 0\n",
      "\t\t\tprocessing order 1 at column 1\n",
      "\t\tpreparing segment 1 (row)\n",
      "\t\t\tprocessing order 0 at column 0\n",
      "\t\t\tprocessing order 1 at column 1\n",
      "\t\tpreparing segment 2 (row)\n",
      "\t\t\tprocessing order 0 at column 0\n",
      "\t\t\tprocessing order 1 at column 1\n",
      "\t\tpreparing segment 3 (row)\n",
      "\t\t\tprocessing order 0 at column 0\n",
      "\t\t\tprocessing order 1 at column 1\n",
      "\t\tpreparing segment 4 (row)\n",
      "\t\t\tprocessing order 0 at column 0\n",
      "\t\t\tprocessing order 1 at column 1\n",
      "\tpreparing group 1\n",
      "\t\tpreparing segment 0 (row)\n",
      "\t\t\tprocessing order 0 at column 2\n",
      "\t\tpreparing segment 1 (row)\n",
      "\t\t\tprocessing order 0 at column 2\n",
      "\t\tpreparing segment 2 (row)\n",
      "\t\t\tprocessing order 0 at column 2\n",
      "\t\tpreparing segment 3 (row)\n",
      "\t\t\tprocessing order 0 at column 2\n",
      "\t\tpreparing segment 4 (row)\n",
      "\t\t\tprocessing order 0 at column 2\n",
      "[1] [2] [10] [15]\n"
     ]
    }
   ],
   "source": [
    "@njit\n",
    "def pre_sim_func_nb(c, sim_i):\n",
    "    print('preparing simulation')\n",
    "    sim_i[0] += 1\n",
    "    return sim_i,\n",
    "\n",
    "@njit\n",
    "def group_pre_sim_func_nb(c, sim_i, group_i):\n",
    "    print('\\tpreparing group', c.group)\n",
    "    group_i[0] += 1\n",
    "    return sim_i, group_i\n",
    "\n",
    "@njit\n",
    "def pre_segment_func_nb(c, sim_i, group_i, segment_i):\n",
    "    print('\\t\\tpreparing segment', c.i, '(row)')\n",
    "    segment_i[0] += 1\n",
    "    return sim_i, group_i, segment_i\n",
    "\n",
    "@njit\n",
    "def order_func_nb(c, sim_i, group_i, segment_i, order_i):\n",
    "    print('\\t\\t\\tprocessing order', c.call_idx, 'at column', c.col)\n",
    "    order_i[0] += 1\n",
    "    return NoOrder\n",
    "\n",
    "sim_i = np.array([0])\n",
    "group_i = np.array([0])\n",
    "segment_i = np.array([0])\n",
    "order_i = np.array([0])\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price_wide, order_func_nb, order_i, \n",
    "    group_by=np.array([0, 0, 1]), \n",
    "    pre_sim_func_nb=pre_sim_func_nb, pre_sim_args=(sim_i,),\n",
    "    pre_group_func_nb=group_pre_sim_func_nb, pre_group_args=(group_i,),\n",
    "    pre_segment_func_nb=pre_segment_func_nb, pre_segment_args=(segment_i,)\n",
    ")\n",
    "print(sim_i, group_i, segment_i, order_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing simulation\n",
      "\tpreparing row 0\n",
      "\t\tpreparing segment 0 (group)\n",
      "\t\t\tprocessing order 0 at column 0\n",
      "\t\t\tprocessing order 1 at column 1\n",
      "\t\tpreparing segment 1 (group)\n",
      "\t\t\tprocessing order 0 at column 2\n",
      "\tpreparing row 1\n",
      "\t\tpreparing segment 0 (group)\n",
      "\t\t\tprocessing order 0 at column 0\n",
      "\t\t\tprocessing order 1 at column 1\n",
      "\t\tpreparing segment 1 (group)\n",
      "\t\t\tprocessing order 0 at column 2\n",
      "\tpreparing row 2\n",
      "\t\tpreparing segment 0 (group)\n",
      "\t\t\tprocessing order 0 at column 0\n",
      "\t\t\tprocessing order 1 at column 1\n",
      "\t\tpreparing segment 1 (group)\n",
      "\t\t\tprocessing order 0 at column 2\n",
      "\tpreparing row 3\n",
      "\t\tpreparing segment 0 (group)\n",
      "\t\t\tprocessing order 0 at column 0\n",
      "\t\t\tprocessing order 1 at column 1\n",
      "\t\tpreparing segment 1 (group)\n",
      "\t\t\tprocessing order 0 at column 2\n",
      "\tpreparing row 4\n",
      "\t\tpreparing segment 0 (group)\n",
      "\t\t\tprocessing order 0 at column 0\n",
      "\t\t\tprocessing order 1 at column 1\n",
      "\t\tpreparing segment 1 (group)\n",
      "\t\t\tprocessing order 0 at column 2\n",
      "[1] [5] [10] [15]\n"
     ]
    }
   ],
   "source": [
    "@njit\n",
    "def pre_sim_func_nb(c, sim_i):\n",
    "    print('preparing simulation')\n",
    "    sim_i[0] += 1\n",
    "    return sim_i,\n",
    "\n",
    "@njit\n",
    "def row_pre_sim_func_nb(c, sim_i, row_i):\n",
    "    print('\\tpreparing row', c.i)\n",
    "    row_i[0] += 1\n",
    "    return sim_i, row_i\n",
    "\n",
    "@njit\n",
    "def pre_segment_func_nb(c, sim_i, row_i, segment_i):\n",
    "    print('\\t\\tpreparing segment', c.group, '(group)')\n",
    "    segment_i[0] += 1\n",
    "    return sim_i, row_i, segment_i\n",
    "\n",
    "@njit\n",
    "def order_func_nb(c, sim_i, row_i, segment_i, order_i):\n",
    "    print('\\t\\t\\tprocessing order', c.call_idx, 'at column', c.col)\n",
    "    order_i[0] += 1\n",
    "    return NoOrder\n",
    "\n",
    "sim_i = np.array([0])\n",
    "row_i = np.array([0])\n",
    "segment_i = np.array([0])\n",
    "order_i = np.array([0])\n",
    "\n",
    "portfolio = vbt.Portfolio.from_order_func(\n",
    "    price_wide, order_func_nb, order_i, \n",
    "    group_by=np.array([0, 0, 1]), \n",
    "    pre_sim_func_nb=pre_sim_func_nb, pre_sim_args=(sim_i,),\n",
    "    pre_row_func_nb=row_pre_sim_func_nb, pre_row_args=(row_i,),\n",
    "    pre_segment_func_nb=pre_segment_func_nb, pre_segment_args=(segment_i,),\n",
    "    row_wise=True\n",
    ")\n",
    "print(sim_i, row_i, segment_i, order_i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## methods and properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_na = pd.DataFrame({\n",
    "    'a': [np.nan, 2., 3., 4., 5.],\n",
    "    'b': [1., 2., np.nan, 4., 5.],\n",
    "    'c': [1., 2., 3., 4., np.nan]\n",
    "}, index=price.index)\n",
    "order_size = pd.Series([1., 0.1, -1., -0.1, 1.])\n",
    "directions = ['longonly', 'shortonly', 'both']\n",
    "group_by = pd.Index(['first', 'first', 'second'], name='group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    id  col  idx  size  price     fees  side\n",
      "0    0    0    1   0.1   2.02  0.10202     0\n",
      "1    1    0    2   0.1   2.97  0.10297     1\n",
      "2    2    0    4   1.0   5.05  0.15050     0\n",
      "3    3    1    0   1.0   0.99  0.10990     1\n",
      "4    4    1    1   0.1   1.98  0.10198     1\n",
      "5    5    1    3   0.1   4.04  0.10404     0\n",
      "6    6    1    4   1.0   4.95  0.14950     1\n",
      "7    7    2    0   1.0   1.01  0.11010     0\n",
      "8    8    2    1   0.1   2.02  0.10202     0\n",
      "9    9    2    2   1.0   2.97  0.12970     1\n",
      "10  10    2    3   0.1   3.96  0.10396     1\n",
      "    id  col  idx  size  price     fees  side\n",
      "0    0    0    1   0.1   2.02  0.10202     0\n",
      "1    1    0    2   0.1   2.97  0.10297     1\n",
      "2    2    0    4   1.0   5.05  0.15050     0\n",
      "3    3    1    0   1.0   0.99  0.10990     1\n",
      "4    4    1    1   0.1   1.98  0.10198     1\n",
      "5    5    1    3   0.1   4.04  0.10404     0\n",
      "6    6    1    4   1.0   4.95  0.14950     1\n",
      "7    7    2    0   1.0   1.01  0.11010     0\n",
      "8    8    2    1   0.1   2.02  0.10202     0\n",
      "9    9    2    2   1.0   2.97  0.12970     1\n",
      "10  10    2    3   0.1   3.96  0.10396     1\n",
      "    id  col  idx  size  price     fees  side\n",
      "0    0    1    0   1.0   0.99  0.10990     1\n",
      "1    1    1    1   0.1   1.98  0.10198     1\n",
      "2    2    0    1   0.1   2.02  0.10202     0\n",
      "3    3    0    2   0.1   2.97  0.10297     1\n",
      "4    4    1    3   0.1   4.04  0.10404     0\n",
      "5    5    1    4   1.0   4.95  0.14950     1\n",
      "6    6    0    4   1.0   5.05  0.15050     0\n",
      "7    7    2    0   1.0   1.01  0.11010     0\n",
      "8    8    2    1   0.1   2.02  0.10202     0\n",
      "9    9    2    2   1.0   2.97  0.12970     1\n",
      "10  10    2    3   0.1   3.96  0.10396     1\n"
     ]
    }
   ],
   "source": [
    "portfolio = vbt.Portfolio.from_orders(\n",
    "    price_na, order_size, size_type='amount', direction=directions,\n",
    "    fees=0.01, fixed_fees=0.1, slippage=0.01, log=True,\n",
    "    call_seq='reversed', group_by=None,\n",
    "    init_cash=[100., 100., 100.], freq='1D'\n",
    ")  # independent\n",
    "print(portfolio.orders.records)\n",
    "\n",
    "portfolio_grouped = vbt.Portfolio.from_orders(\n",
    "    price_na, order_size, size_type='amount', direction=directions,\n",
    "    fees=0.01, fixed_fees=0.1, slippage=0.01, log=True,\n",
    "    call_seq='reversed', group_by=group_by, cash_sharing=False,\n",
    "    init_cash=[100., 100., 100.], freq='1D'\n",
    ")  # grouped\n",
    "print(portfolio_grouped.orders.records)\n",
    "\n",
    "portfolio_shared = vbt.Portfolio.from_orders(\n",
    "    price_na, order_size, size_type='amount', direction=directions,\n",
    "    fees=0.01, fixed_fees=0.1, slippage=0.01, log=True,\n",
    "    call_seq='reversed', group_by=group_by, cash_sharing=True,\n",
    "    init_cash=[200., 100.], freq='1D'\n",
    ")  # shared\n",
    "print(portfolio_shared.orders.records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000000,)\n",
      "(1000000,)\n",
      "(1000000,)\n"
     ]
    }
   ],
   "source": [
    "big_group_by = np.repeat(np.arange(500), 2)\n",
    "big_portfolio = vbt.Portfolio.from_orders(\n",
    "    big_price_wide, big_order_size)\n",
    "print(big_portfolio.orders.values.shape)\n",
    "\n",
    "big_portfolio_grouped = vbt.Portfolio.from_orders(\n",
    "    big_price_wide, big_order_size, group_by=big_group_by)\n",
    "print(big_portfolio_grouped.orders.values.shape)\n",
    "\n",
    "big_portfolio_shared = vbt.Portfolio.from_orders(\n",
    "    big_price_wide, big_order_size, group_by=big_group_by, cash_sharing=True)\n",
    "print(big_portfolio_shared.orders.values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',\n",
      "               '2020-01-05'],\n",
      "              dtype='datetime64[ns]', freq=None)\n",
      "Index(['a', 'b', 'c'], dtype='object')\n",
      "2\n",
      "None\n",
      "True\n",
      "True\n",
      "True\n",
      "DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',\n",
      "               '2020-01-05'],\n",
      "              dtype='datetime64[ns]', freq=None)\n",
      "Index(['a', 'b', 'c'], dtype='object')\n",
      "2\n",
      "Index(['first', 'first', 'second'], dtype='object', name='group')\n",
      "True\n",
      "True\n",
      "True\n",
      "DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',\n",
      "               '2020-01-05'],\n",
      "              dtype='datetime64[ns]', freq=None)\n",
      "Index(['a', 'b', 'c'], dtype='object')\n",
      "2\n",
      "Index(['first', 'first', 'second'], dtype='object', name='group')\n",
      "False\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.wrapper.index)\n",
    "print(portfolio.wrapper.columns)\n",
    "print(portfolio.wrapper.ndim)\n",
    "print(portfolio.wrapper.grouper.group_by)\n",
    "print(portfolio.wrapper.grouper.allow_enable)\n",
    "print(portfolio.wrapper.grouper.allow_disable)\n",
    "print(portfolio.wrapper.grouper.allow_modify)\n",
    "\n",
    "print(portfolio_grouped.wrapper.index)\n",
    "print(portfolio_grouped.wrapper.columns)\n",
    "print(portfolio_grouped.wrapper.ndim)\n",
    "print(portfolio_grouped.wrapper.grouper.group_by)\n",
    "print(portfolio_grouped.wrapper.grouper.allow_enable)\n",
    "print(portfolio_grouped.wrapper.grouper.allow_disable)\n",
    "print(portfolio_grouped.wrapper.grouper.allow_modify)\n",
    "\n",
    "print(portfolio_shared.wrapper.index)\n",
    "print(portfolio_shared.wrapper.columns)\n",
    "print(portfolio_shared.wrapper.ndim)\n",
    "print(portfolio_shared.wrapper.grouper.group_by)\n",
    "print(portfolio_shared.wrapper.grouper.allow_enable)\n",
    "print(portfolio_shared.wrapper.grouper.allow_disable)\n",
    "print(portfolio_shared.wrapper.grouper.allow_modify)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    id  idx  col  size  price     fees  side\n",
      "0    0    1    0   0.1   2.02  0.10202     0\n",
      "1    1    2    0   0.1   2.97  0.10297     1\n",
      "2    2    4    0   1.0   5.05  0.15050     0\n",
      "3    3    0    1   1.0   0.99  0.10990     1\n",
      "4    4    1    1   0.1   1.98  0.10198     1\n",
      "5    5    3    1   0.1   4.04  0.10404     0\n",
      "6    6    4    1   1.0   4.95  0.14950     1\n",
      "7    7    0    2   1.0   1.01  0.11010     0\n",
      "8    8    1    2   0.1   2.02  0.10202     0\n",
      "9    9    2    2   1.0   2.97  0.12970     1\n",
      "10  10    3    2   0.1   3.96  0.10396     1\n",
      "a    3\n",
      "b    4\n",
      "c    4\n",
      "Name: count, dtype: int64\n",
      "a    5\n",
      "b    5\n",
      "c    5\n",
      "Name: count, dtype: int64\n",
      "              a    b    c\n",
      "2020-01-01  NaN  1.0  1.0\n",
      "2020-01-02  2.0  2.0  2.0\n",
      "2020-01-03  3.0  NaN  3.0\n",
      "2020-01-04  4.0  4.0  4.0\n",
      "2020-01-05  5.0  5.0  NaN\n",
      "a    100.0\n",
      "b    100.0\n",
      "c    100.0\n",
      "Name: init_cash, dtype: float64\n",
      "                    a         b          c\n",
      "2020-01-01  100.00000  99.88010   99.87990\n",
      "2020-01-02   99.89598  98.77612  100.77588\n",
      "2020-01-03   99.89001  98.77612  101.71618\n",
      "2020-01-04   99.89001  96.46808  101.70822\n",
      "2020-01-05   99.68951  95.26858  101.70822\n",
      "a     99.68951\n",
      "b     95.26858\n",
      "c    101.70822\n",
      "Name: final_value, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.orders.records)\n",
    "print(portfolio.orders.count())\n",
    "print(portfolio.logs.count())\n",
    "print(portfolio.close)\n",
    "print(portfolio.init_cash)\n",
    "print(portfolio.value())\n",
    "print(portfolio.final_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col  size  price     fees  side\n",
      "0   0    1    0   0.1   2.02  0.10202     0\n",
      "1   1    2    0   0.1   2.97  0.10297     1\n",
      "2   2    4    0   1.0   5.05  0.15050     0\n",
      "3\n",
      "5\n",
      "2020-01-01    NaN\n",
      "2020-01-02    2.0\n",
      "2020-01-03    3.0\n",
      "2020-01-04    4.0\n",
      "2020-01-05    5.0\n",
      "Name: a, dtype: float64\n",
      "100.0\n",
      "2020-01-01    100.00000\n",
      "2020-01-02     99.89598\n",
      "2020-01-03     99.89001\n",
      "2020-01-04     99.89001\n",
      "2020-01-05     99.68951\n",
      "Name: a, dtype: float64\n",
      "99.68951\n"
     ]
    }
   ],
   "source": [
    "print(portfolio['a'].orders.records)\n",
    "print(portfolio['a'].orders.count())\n",
    "print(portfolio['a'].logs.count())\n",
    "print(portfolio['a'].close)\n",
    "print(portfolio['a'].init_cash)\n",
    "print(portfolio['a'].value())\n",
    "print(portfolio['a'].final_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col  size  price     fees  side\n",
      "0   7    0    0   1.0   1.01  0.11010     0\n",
      "1   8    1    0   0.1   2.02  0.10202     0\n",
      "2   9    2    0   1.0   2.97  0.12970     1\n",
      "3  10    3    0   0.1   3.96  0.10396     1\n",
      "4\n",
      "5\n",
      "2020-01-01    1.0\n",
      "2020-01-02    2.0\n",
      "2020-01-03    3.0\n",
      "2020-01-04    4.0\n",
      "2020-01-05    NaN\n",
      "Name: c, dtype: float64\n",
      "100.0\n",
      "2020-01-01     99.87990\n",
      "2020-01-02    100.77588\n",
      "2020-01-03    101.71618\n",
      "2020-01-04    101.70822\n",
      "2020-01-05    101.70822\n",
      "Name: c, dtype: float64\n",
      "101.70822\n"
     ]
    }
   ],
   "source": [
    "print(portfolio['c'].orders.records)\n",
    "print(portfolio['c'].orders.count())\n",
    "print(portfolio['c'].logs.count())\n",
    "print(portfolio['c'].close)\n",
    "print(portfolio['c'].init_cash)\n",
    "print(portfolio['c'].value())\n",
    "print(portfolio['c'].final_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col  size  price     fees  side\n",
      "0   7    0    0   1.0   1.01  0.11010     0\n",
      "1   8    1    0   0.1   2.02  0.10202     0\n",
      "2   9    2    0   1.0   2.97  0.12970     1\n",
      "3  10    3    0   0.1   3.96  0.10396     1\n",
      "c    4\n",
      "Name: count, dtype: int64\n",
      "c    5\n",
      "Name: count, dtype: int64\n",
      "              c\n",
      "2020-01-01  1.0\n",
      "2020-01-02  2.0\n",
      "2020-01-03  3.0\n",
      "2020-01-04  4.0\n",
      "2020-01-05  NaN\n",
      "c    100.0\n",
      "Name: init_cash, dtype: float64\n",
      "                    c\n",
      "2020-01-01   99.87990\n",
      "2020-01-02  100.77588\n",
      "2020-01-03  101.71618\n",
      "2020-01-04  101.70822\n",
      "2020-01-05  101.70822\n",
      "c    101.70822\n",
      "Name: final_value, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio[['c']].orders.records)\n",
    "print(portfolio[['c']].orders.count())\n",
    "print(portfolio[['c']].logs.count())\n",
    "print(portfolio[['c']].close)\n",
    "print(portfolio[['c']].init_cash)\n",
    "print(portfolio[['c']].value())\n",
    "print(portfolio[['c']].final_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    id  idx  col  size  price     fees  side\n",
      "0    0    1    0   0.1   2.02  0.10202     0\n",
      "1    1    2    0   0.1   2.97  0.10297     1\n",
      "2    2    4    0   1.0   5.05  0.15050     0\n",
      "3    3    0    1   1.0   0.99  0.10990     1\n",
      "4    4    1    1   0.1   1.98  0.10198     1\n",
      "5    5    3    1   0.1   4.04  0.10404     0\n",
      "6    6    4    1   1.0   4.95  0.14950     1\n",
      "7    7    0    2   1.0   1.01  0.11010     0\n",
      "8    8    1    2   0.1   2.02  0.10202     0\n",
      "9    9    2    2   1.0   2.97  0.12970     1\n",
      "10  10    3    2   0.1   3.96  0.10396     1\n",
      "group\n",
      "first     7\n",
      "second    4\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     10\n",
      "second     5\n",
      "Name: count, dtype: int64\n",
      "              a    b    c\n",
      "2020-01-01  NaN  1.0  1.0\n",
      "2020-01-02  2.0  2.0  2.0\n",
      "2020-01-03  3.0  NaN  3.0\n",
      "2020-01-04  4.0  4.0  4.0\n",
      "2020-01-05  5.0  5.0  NaN\n",
      "group\n",
      "first     200.0\n",
      "second    100.0\n",
      "Name: init_cash, dtype: float64\n",
      "group           first     second\n",
      "2020-01-01  199.88010   99.87990\n",
      "2020-01-02  198.67210  100.77588\n",
      "2020-01-03  198.66613  101.71618\n",
      "2020-01-04  196.35809  101.70822\n",
      "2020-01-05  194.95809  101.70822\n",
      "group\n",
      "first     194.95809\n",
      "second    101.70822\n",
      "Name: final_value, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio_grouped.orders.records)\n",
    "print(portfolio_grouped.orders.count())\n",
    "print(portfolio_grouped.logs.count())\n",
    "print(portfolio_grouped.close)\n",
    "print(portfolio_grouped.init_cash)\n",
    "print(portfolio_grouped.value())\n",
    "print(portfolio_grouped.final_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col  size  price     fees  side\n",
      "0   0    1    0   0.1   2.02  0.10202     0\n",
      "1   1    2    0   0.1   2.97  0.10297     1\n",
      "2   2    4    0   1.0   5.05  0.15050     0\n",
      "3   3    0    1   1.0   0.99  0.10990     1\n",
      "4   4    1    1   0.1   1.98  0.10198     1\n",
      "5   5    3    1   0.1   4.04  0.10404     0\n",
      "6   6    4    1   1.0   4.95  0.14950     1\n",
      "7\n",
      "10\n",
      "              a    b\n",
      "2020-01-01  NaN  1.0\n",
      "2020-01-02  2.0  2.0\n",
      "2020-01-03  3.0  NaN\n",
      "2020-01-04  4.0  4.0\n",
      "2020-01-05  5.0  5.0\n",
      "200.0\n",
      "2020-01-01    199.88010\n",
      "2020-01-02    198.67210\n",
      "2020-01-03    196.46613\n",
      "2020-01-04    196.35809\n",
      "2020-01-05    194.95809\n",
      "Name: first, dtype: float64\n",
      "194.95809\n"
     ]
    }
   ],
   "source": [
    "print(portfolio_grouped['first'].orders.records)\n",
    "print(portfolio_grouped['first'].orders.count())\n",
    "print(portfolio_grouped['first'].logs.count())\n",
    "print(portfolio_grouped['first'].close)\n",
    "print(portfolio_grouped['first'].init_cash)\n",
    "print(portfolio_grouped['first'].value())\n",
    "print(portfolio_grouped['first'].final_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col  size  price     fees  side\n",
      "0   7    0    0   1.0   1.01  0.11010     0\n",
      "1   8    1    0   0.1   2.02  0.10202     0\n",
      "2   9    2    0   1.0   2.97  0.12970     1\n",
      "3  10    3    0   0.1   3.96  0.10396     1\n",
      "4\n",
      "5\n",
      "2020-01-01    1.0\n",
      "2020-01-02    2.0\n",
      "2020-01-03    3.0\n",
      "2020-01-04    4.0\n",
      "2020-01-05    NaN\n",
      "Name: c, dtype: float64\n",
      "100.0\n",
      "2020-01-01     99.87990\n",
      "2020-01-02    100.77588\n",
      "2020-01-03    101.71618\n",
      "2020-01-04    101.70822\n",
      "2020-01-05    101.70822\n",
      "Name: second, dtype: float64\n",
      "101.70822\n"
     ]
    }
   ],
   "source": [
    "print(portfolio_grouped['second'].orders.records)\n",
    "print(portfolio_grouped['second'].orders.count())\n",
    "print(portfolio_grouped['second'].logs.count())\n",
    "print(portfolio_grouped['second'].close)\n",
    "print(portfolio_grouped['second'].init_cash)\n",
    "print(portfolio_grouped['second'].value())\n",
    "print(portfolio_grouped['second'].final_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id  idx  col  size  price     fees  side\n",
      "0   7    0    0   1.0   1.01  0.11010     0\n",
      "1   8    1    0   0.1   2.02  0.10202     0\n",
      "2   9    2    0   1.0   2.97  0.12970     1\n",
      "3  10    3    0   0.1   3.96  0.10396     1\n",
      "group\n",
      "second    4\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "second    5\n",
      "Name: count, dtype: int64\n",
      "              c\n",
      "2020-01-01  1.0\n",
      "2020-01-02  2.0\n",
      "2020-01-03  3.0\n",
      "2020-01-04  4.0\n",
      "2020-01-05  NaN\n",
      "group\n",
      "second    100.0\n",
      "Name: init_cash, dtype: float64\n",
      "group          second\n",
      "2020-01-01   99.87990\n",
      "2020-01-02  100.77588\n",
      "2020-01-03  101.71618\n",
      "2020-01-04  101.70822\n",
      "2020-01-05  101.70822\n",
      "group\n",
      "second    101.70822\n",
      "Name: final_value, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio_grouped[['second']].orders.records)\n",
    "print(portfolio_grouped[['second']].orders.count())\n",
    "print(portfolio_grouped[['second']].logs.count())\n",
    "print(portfolio_grouped[['second']].close)\n",
    "print(portfolio_grouped[['second']].init_cash)\n",
    "print(portfolio_grouped[['second']].value())\n",
    "print(portfolio_grouped[['second']].final_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.42 ms ± 283 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "The slowest run took 17.43 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "111 ms ± 89.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "3.37 ms ± 46.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "14.3 ms ± 1.99 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "6.41 ms ± 139 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "18.1 ms ± 366 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.iloc[0]\n",
    "%timeit big_portfolio.iloc[:]\n",
    "%timeit big_portfolio_grouped.iloc[0]\n",
    "%timeit big_portfolio_grouped.iloc[:]\n",
    "%timeit big_portfolio_shared.iloc[0]\n",
    "%timeit big_portfolio_shared.iloc[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.cash_sharing)\n",
    "print(portfolio_grouped.cash_sharing)\n",
    "print(portfolio_shared.cash_sharing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            a  b  c\n",
      "2020-01-01  0  0  0\n",
      "2020-01-02  0  0  0\n",
      "2020-01-03  0  0  0\n",
      "2020-01-04  0  0  0\n",
      "2020-01-05  0  0  0\n",
      "            a  b  c\n",
      "2020-01-01  1  0  0\n",
      "2020-01-02  1  0  0\n",
      "2020-01-03  1  0  0\n",
      "2020-01-04  1  0  0\n",
      "2020-01-05  1  0  0\n",
      "            a  b  c\n",
      "2020-01-01  1  0  0\n",
      "2020-01-02  1  0  0\n",
      "2020-01-03  1  0  0\n",
      "2020-01-04  1  0  0\n",
      "2020-01-05  1  0  0\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.call_seq)\n",
    "print(portfolio_grouped.call_seq)\n",
    "print(portfolio_shared.call_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    3\n",
      "b    4\n",
      "c    4\n",
      "Name: count, dtype: int64\n",
      "a    3\n",
      "b    4\n",
      "c    4\n",
      "Name: count, dtype: int64\n",
      "a    3\n",
      "b    4\n",
      "c    4\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     7\n",
      "second    4\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     7\n",
      "second    4\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     7\n",
      "second    4\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.orders.count())\n",
    "print(portfolio_grouped.get_orders(group_by=False).count())\n",
    "print(portfolio_shared.get_orders(group_by=False).count())\n",
    "\n",
    "print(portfolio.get_orders(group_by=group_by).count())\n",
    "print(portfolio_grouped.orders.count())\n",
    "print(portfolio_shared.orders.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    5\n",
      "b    5\n",
      "c    5\n",
      "Name: count, dtype: int64\n",
      "a    5\n",
      "b    5\n",
      "c    5\n",
      "Name: count, dtype: int64\n",
      "a    5\n",
      "b    5\n",
      "c    5\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     10\n",
      "second     5\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     10\n",
      "second     5\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     10\n",
      "second     5\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.logs.count())\n",
    "print(portfolio_grouped.get_logs(group_by=False).count())\n",
    "print(portfolio_shared.get_logs(group_by=False).count())\n",
    "\n",
    "print(portfolio.get_logs(group_by=group_by).count())\n",
    "print(portfolio_grouped.logs.count())\n",
    "print(portfolio_shared.logs.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    2\n",
      "b    3\n",
      "c    2\n",
      "Name: count, dtype: int64\n",
      "a    2\n",
      "b    3\n",
      "c    2\n",
      "Name: count, dtype: int64\n",
      "a    2\n",
      "b    3\n",
      "c    2\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     5\n",
      "second    2\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     5\n",
      "second    2\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     5\n",
      "second    2\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.entry_trades.count())\n",
    "print(portfolio_grouped.get_entry_trades(group_by=False).count())\n",
    "print(portfolio_shared.get_entry_trades(group_by=False).count())\n",
    "\n",
    "print(portfolio.get_entry_trades(group_by=group_by).count())\n",
    "print(portfolio_grouped.entry_trades.count())\n",
    "print(portfolio_shared.entry_trades.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "397 ms ± 3.87 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.entry_trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    2\n",
      "b    2\n",
      "c    2\n",
      "Name: count, dtype: int64\n",
      "a    2\n",
      "b    2\n",
      "c    2\n",
      "Name: count, dtype: int64\n",
      "a    2\n",
      "b    2\n",
      "c    2\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     4\n",
      "second    2\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     4\n",
      "second    2\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     4\n",
      "second    2\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.exit_trades.count())\n",
    "print(portfolio_grouped.get_exit_trades(group_by=False).count())\n",
    "print(portfolio_shared.get_exit_trades(group_by=False).count())\n",
    "\n",
    "print(portfolio.get_exit_trades(group_by=group_by).count())\n",
    "print(portfolio_grouped.exit_trades.count())\n",
    "print(portfolio_shared.exit_trades.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.8 ms ± 607 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.exit_trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    2\n",
      "b    1\n",
      "c    1\n",
      "Name: count, dtype: int64\n",
      "a    2\n",
      "b    1\n",
      "c    1\n",
      "Name: count, dtype: int64\n",
      "a    2\n",
      "b    1\n",
      "c    1\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     3\n",
      "second    1\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     3\n",
      "second    1\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     3\n",
      "second    1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.positions.count())\n",
    "print(portfolio_grouped.get_positions(group_by=False).count())\n",
    "print(portfolio_shared.get_positions(group_by=False).count())\n",
    "\n",
    "print(portfolio.get_positions(group_by=group_by).count())\n",
    "print(portfolio_grouped.positions.count())\n",
    "print(portfolio_shared.positions.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.4 ms ± 3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    1\n",
      "c    1\n",
      "Name: count, dtype: int64\n",
      "a    1\n",
      "b    1\n",
      "c    1\n",
      "Name: count, dtype: int64\n",
      "a    1\n",
      "b    1\n",
      "c    1\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     1\n",
      "second    1\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     1\n",
      "second    1\n",
      "Name: count, dtype: int64\n",
      "group\n",
      "first     1\n",
      "second    1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.drawdowns.count())\n",
    "print(portfolio_grouped.get_drawdowns(group_by=False).count())\n",
    "print(portfolio_shared.get_drawdowns(group_by=False).count())\n",
    "\n",
    "print(portfolio.get_drawdowns(group_by=group_by).count())\n",
    "print(portfolio_grouped.drawdowns.count())\n",
    "print(portfolio_shared.drawdowns.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.9 ms ± 907 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.drawdowns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              a    b    c\n",
      "2020-01-01  NaN  1.0  1.0\n",
      "2020-01-02  2.0  2.0  2.0\n",
      "2020-01-03  3.0  NaN  3.0\n",
      "2020-01-04  4.0  4.0  4.0\n",
      "2020-01-05  5.0  5.0  NaN\n",
      "              a    b    c\n",
      "2020-01-01  NaN  1.0  1.0\n",
      "2020-01-02  2.0  2.0  2.0\n",
      "2020-01-03  3.0  NaN  3.0\n",
      "2020-01-04  4.0  4.0  4.0\n",
      "2020-01-05  5.0  5.0  NaN\n",
      "              a    b    c\n",
      "2020-01-01  NaN  1.0  1.0\n",
      "2020-01-02  2.0  2.0  2.0\n",
      "2020-01-03  3.0  NaN  3.0\n",
      "2020-01-04  4.0  4.0  4.0\n",
      "2020-01-05  5.0  5.0  NaN\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.close)\n",
    "print(portfolio_grouped.close)\n",
    "print(portfolio_shared.close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              a    b    c\n",
      "2020-01-01  NaN  1.0  1.0\n",
      "2020-01-02  2.0  2.0  2.0\n",
      "2020-01-03  3.0  NaN  3.0\n",
      "2020-01-04  4.0  4.0  4.0\n",
      "2020-01-05  5.0  5.0  NaN\n",
      "              a    b    c\n",
      "2020-01-01  NaN  1.0  1.0\n",
      "2020-01-02  2.0  2.0  2.0\n",
      "2020-01-03  3.0  2.0  3.0\n",
      "2020-01-04  4.0  4.0  4.0\n",
      "2020-01-05  5.0  5.0  4.0\n",
      "              a    b    c\n",
      "2020-01-01  2.0  1.0  1.0\n",
      "2020-01-02  2.0  2.0  2.0\n",
      "2020-01-03  3.0  4.0  3.0\n",
      "2020-01-04  4.0  4.0  4.0\n",
      "2020-01-05  5.0  5.0  NaN\n",
      "              a    b    c\n",
      "2020-01-01  2.0  1.0  1.0\n",
      "2020-01-02  2.0  2.0  2.0\n",
      "2020-01-03  3.0  2.0  3.0\n",
      "2020-01-04  4.0  4.0  4.0\n",
      "2020-01-05  5.0  5.0  4.0\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.fill_close(ffill=False, bfill=False))\n",
    "print(portfolio.fill_close(ffill=True, bfill=False))\n",
    "print(portfolio.fill_close(ffill=False, bfill=True))\n",
    "print(portfolio.fill_close(ffill=True, bfill=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67.6 µs ± 1.39 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.fill_close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              a    b    c\n",
      "2020-01-01  0.0  0.0  1.0\n",
      "2020-01-02  0.1  0.0  0.1\n",
      "2020-01-03 -0.1  0.0 -1.0\n",
      "2020-01-04  0.0  0.0 -0.1\n",
      "2020-01-05  1.0  0.0  0.0\n",
      "              a    b    c\n",
      "2020-01-01  0.0  1.0  0.0\n",
      "2020-01-02  0.0  0.1  0.0\n",
      "2020-01-03  0.0  0.0  0.0\n",
      "2020-01-04  0.0 -0.1  0.0\n",
      "2020-01-05  0.0  1.0  0.0\n",
      "              a    b    c\n",
      "2020-01-01  0.0 -1.0  1.0\n",
      "2020-01-02  0.1 -0.1  0.1\n",
      "2020-01-03 -0.1  0.0 -1.0\n",
      "2020-01-04  0.0  0.1 -0.1\n",
      "2020-01-05  1.0 -1.0  0.0\n",
      "              a    b    c\n",
      "2020-01-01  0.0 -1.0  1.0\n",
      "2020-01-02  0.1 -0.1  0.1\n",
      "2020-01-03 -0.1  0.0 -1.0\n",
      "2020-01-04  0.0  0.1 -0.1\n",
      "2020-01-05  1.0 -1.0  0.0\n",
      "              a    b    c\n",
      "2020-01-01  0.0 -1.0  1.0\n",
      "2020-01-02  0.1 -0.1  0.1\n",
      "2020-01-03 -0.1  0.0 -1.0\n",
      "2020-01-04  0.0  0.1 -0.1\n",
      "2020-01-05  1.0 -1.0  0.0\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.asset_flow(direction='longonly'))\n",
    "print(portfolio.asset_flow(direction='shortonly'))\n",
    "\n",
    "print(portfolio.asset_flow())\n",
    "print(portfolio_grouped.asset_flow())\n",
    "print(portfolio_shared.asset_flow())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 ms ± 539 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "10.6 ms ± 276 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "10.5 ms ± 148 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.asset_flow()\n",
    "%timeit big_portfolio_grouped.asset_flow()\n",
    "%timeit big_portfolio_shared.asset_flow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              a    b    c\n",
      "2020-01-01  0.0  0.0  1.0\n",
      "2020-01-02  0.1  0.0  1.1\n",
      "2020-01-03  0.0  0.0  0.1\n",
      "2020-01-04  0.0  0.0  0.0\n",
      "2020-01-05  1.0  0.0  0.0\n",
      "              a    b    c\n",
      "2020-01-01  0.0  1.0  0.0\n",
      "2020-01-02  0.0  1.1  0.0\n",
      "2020-01-03  0.0  1.1  0.0\n",
      "2020-01-04  0.0  1.0  0.0\n",
      "2020-01-05  0.0  2.0  0.0\n",
      "              a    b    c\n",
      "2020-01-01  0.0 -1.0  1.0\n",
      "2020-01-02  0.1 -1.1  1.1\n",
      "2020-01-03  0.0 -1.1  0.1\n",
      "2020-01-04  0.0 -1.0  0.0\n",
      "2020-01-05  1.0 -2.0  0.0\n",
      "              a    b    c\n",
      "2020-01-01  0.0 -1.0  1.0\n",
      "2020-01-02  0.1 -1.1  1.1\n",
      "2020-01-03  0.0 -1.1  0.1\n",
      "2020-01-04  0.0 -1.0  0.0\n",
      "2020-01-05  1.0 -2.0  0.0\n",
      "              a    b    c\n",
      "2020-01-01  0.0 -1.0  1.0\n",
      "2020-01-02  0.1 -1.1  1.1\n",
      "2020-01-03  0.0 -1.1  0.1\n",
      "2020-01-04  0.0 -1.0  0.0\n",
      "2020-01-05  1.0 -2.0  0.0\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.assets(direction='longonly'))\n",
    "print(portfolio.assets(direction='shortonly'))\n",
    "\n",
    "print(portfolio.assets())\n",
    "print(portfolio_grouped.assets())\n",
    "print(portfolio_shared.assets())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.4 ms ± 539 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "14.6 ms ± 265 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "14 ms ± 50.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.assets()\n",
    "%timeit big_portfolio_grouped.assets()\n",
    "%timeit big_portfolio_shared.assets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                a      b      c\n",
      "2020-01-01  False  False   True\n",
      "2020-01-02   True  False   True\n",
      "2020-01-03  False  False   True\n",
      "2020-01-04  False  False  False\n",
      "2020-01-05   True  False  False\n",
      "                a     b      c\n",
      "2020-01-01  False  True  False\n",
      "2020-01-02  False  True  False\n",
      "2020-01-03  False  True  False\n",
      "2020-01-04  False  True  False\n",
      "2020-01-05  False  True  False\n",
      "                a     b      c\n",
      "2020-01-01  False  True   True\n",
      "2020-01-02   True  True   True\n",
      "2020-01-03  False  True   True\n",
      "2020-01-04  False  True  False\n",
      "2020-01-05   True  True  False\n",
      "                a     b      c\n",
      "2020-01-01  False  True   True\n",
      "2020-01-02   True  True   True\n",
      "2020-01-03  False  True   True\n",
      "2020-01-04  False  True  False\n",
      "2020-01-05   True  True  False\n",
      "                a     b      c\n",
      "2020-01-01  False  True   True\n",
      "2020-01-02   True  True   True\n",
      "2020-01-03  False  True   True\n",
      "2020-01-04  False  True  False\n",
      "2020-01-05   True  True  False\n",
      "group       first  second\n",
      "2020-01-01   True    True\n",
      "2020-01-02   True    True\n",
      "2020-01-03   True    True\n",
      "2020-01-04   True   False\n",
      "2020-01-05   True   False\n",
      "group       first  second\n",
      "2020-01-01   True    True\n",
      "2020-01-02   True    True\n",
      "2020-01-03   True    True\n",
      "2020-01-04   True   False\n",
      "2020-01-05   True   False\n",
      "group       first  second\n",
      "2020-01-01   True    True\n",
      "2020-01-02   True    True\n",
      "2020-01-03   True    True\n",
      "2020-01-04   True   False\n",
      "2020-01-05   True   False\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.position_mask(direction='longonly'))\n",
    "print(portfolio.position_mask(direction='shortonly'))\n",
    "\n",
    "print(portfolio.position_mask())\n",
    "print(portfolio_grouped.position_mask(group_by=False))\n",
    "print(portfolio_shared.position_mask(group_by=False))\n",
    "\n",
    "print(portfolio.position_mask(group_by=group_by))\n",
    "print(portfolio_grouped.position_mask())\n",
    "print(portfolio_shared.position_mask())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.8 ms ± 113 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "15.1 ms ± 462 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "15.6 ms ± 299 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "31.4 ms ± 275 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "31 ms ± 198 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "32 ms ± 208 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.position_mask()\n",
    "%timeit big_portfolio_grouped.position_mask(group_by=False)\n",
    "%timeit big_portfolio_shared.position_mask(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.position_mask(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.position_mask()\n",
    "%timeit big_portfolio_shared.position_mask()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    0.4\n",
      "b    0.0\n",
      "c    0.6\n",
      "Name: position_coverage, dtype: float64\n",
      "a    0.0\n",
      "b    1.0\n",
      "c    0.0\n",
      "Name: position_coverage, dtype: float64\n",
      "a    0.4\n",
      "b    1.0\n",
      "c    0.6\n",
      "Name: position_coverage, dtype: float64\n",
      "a    0.4\n",
      "b    1.0\n",
      "c    0.6\n",
      "Name: position_coverage, dtype: float64\n",
      "a    0.4\n",
      "b    1.0\n",
      "c    0.6\n",
      "Name: position_coverage, dtype: float64\n",
      "group\n",
      "first     0.7\n",
      "second    0.6\n",
      "Name: position_coverage, dtype: float64\n",
      "group\n",
      "first     0.7\n",
      "second    0.6\n",
      "Name: position_coverage, dtype: float64\n",
      "group\n",
      "first     0.7\n",
      "second    0.6\n",
      "Name: position_coverage, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.position_coverage(direction='longonly'))\n",
    "print(portfolio.position_coverage(direction='shortonly'))\n",
    "\n",
    "print(portfolio.position_coverage())\n",
    "print(portfolio_grouped.position_coverage(group_by=False))\n",
    "print(portfolio_shared.position_coverage(group_by=False))\n",
    "\n",
    "print(portfolio.position_coverage(group_by=group_by))\n",
    "print(portfolio_grouped.position_coverage())\n",
    "print(portfolio_shared.position_coverage())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.6 ms ± 1.51 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "14.8 ms ± 104 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "15 ms ± 116 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "34.1 ms ± 1.49 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "32.2 ms ± 1.15 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "33.4 ms ± 1.19 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.position_coverage()\n",
    "%timeit big_portfolio_grouped.position_coverage(group_by=False)\n",
    "%timeit big_portfolio_shared.position_coverage(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.position_coverage(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.position_coverage()\n",
    "%timeit big_portfolio_shared.position_coverage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  a        b        c\n",
      "2020-01-01  0.00000 -1.09990 -1.12010\n",
      "2020-01-02 -0.30402 -0.29998 -0.30402\n",
      "2020-01-03  0.19403  0.00000  2.84030\n",
      "2020-01-04  0.00000 -0.29204  0.29204\n",
      "2020-01-05 -5.20050 -5.09950  0.00000\n",
      "                  a        b        c\n",
      "2020-01-01  0.00000  0.88010 -1.12010\n",
      "2020-01-02 -0.30402  0.09602 -0.30402\n",
      "2020-01-03  0.19403  0.00000  2.84030\n",
      "2020-01-04  0.00000 -0.50804  0.29204\n",
      "2020-01-05 -5.20050  4.80050  0.00000\n",
      "                  a        b        c\n",
      "2020-01-01  0.00000  0.88010 -1.12010\n",
      "2020-01-02 -0.30402  0.09602 -0.30402\n",
      "2020-01-03  0.19403  0.00000  2.84030\n",
      "2020-01-04  0.00000 -0.50804  0.29204\n",
      "2020-01-05 -5.20050  4.80050  0.00000\n",
      "                  a        b        c\n",
      "2020-01-01  0.00000  0.88010 -1.12010\n",
      "2020-01-02 -0.30402  0.09602 -0.30402\n",
      "2020-01-03  0.19403  0.00000  2.84030\n",
      "2020-01-04  0.00000 -0.50804  0.29204\n",
      "2020-01-05 -5.20050  4.80050  0.00000\n",
      "group         first   second\n",
      "2020-01-01  0.88010 -1.12010\n",
      "2020-01-02 -0.20800 -0.30402\n",
      "2020-01-03  0.19403  2.84030\n",
      "2020-01-04 -0.50804  0.29204\n",
      "2020-01-05 -0.40000  0.00000\n",
      "group         first   second\n",
      "2020-01-01  0.88010 -1.12010\n",
      "2020-01-02 -0.20800 -0.30402\n",
      "2020-01-03  0.19403  2.84030\n",
      "2020-01-04 -0.50804  0.29204\n",
      "2020-01-05 -0.40000  0.00000\n",
      "group         first   second\n",
      "2020-01-01  0.88010 -1.12010\n",
      "2020-01-02 -0.20800 -0.30402\n",
      "2020-01-03  0.19403  2.84030\n",
      "2020-01-04 -0.50804  0.29204\n",
      "2020-01-05 -0.40000  0.00000\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.cash_flow(free=True))\n",
    "print(portfolio.cash_flow())\n",
    "print(portfolio_grouped.cash_flow(group_by=False))\n",
    "print(portfolio_shared.cash_flow(group_by=False))\n",
    "\n",
    "print(portfolio.cash_flow(group_by=group_by))\n",
    "print(portfolio_grouped.cash_flow())\n",
    "print(portfolio_shared.cash_flow())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.4 ms ± 912 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "20.4 ms ± 144 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "14.6 ms ± 88.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "16.3 ms ± 81.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "17.9 ms ± 126 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "16.7 ms ± 118 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "20.1 ms ± 155 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.cash_flow()\n",
    "%timeit big_portfolio.cash_flow(free=True)\n",
    "%timeit big_portfolio_grouped.cash_flow(group_by=False)\n",
    "%timeit big_portfolio_shared.cash_flow(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.cash_flow(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.cash_flow()\n",
    "%timeit big_portfolio_shared.cash_flow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    100.0\n",
      "b    100.0\n",
      "c    100.0\n",
      "Name: init_cash, dtype: float64\n",
      "a    100.0\n",
      "b    100.0\n",
      "c    100.0\n",
      "Name: init_cash, dtype: float64\n",
      "a    200.0\n",
      "b    200.0\n",
      "c    100.0\n",
      "Name: init_cash, dtype: float64\n",
      "group\n",
      "first     200.0\n",
      "second    100.0\n",
      "Name: init_cash, dtype: float64\n",
      "group\n",
      "first     200.0\n",
      "second    100.0\n",
      "Name: init_cash, dtype: float64\n",
      "group\n",
      "first     200.0\n",
      "second    100.0\n",
      "Name: init_cash, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.get_init_cash())\n",
    "print(portfolio_grouped.get_init_cash(group_by=False))\n",
    "print(portfolio_shared.get_init_cash(group_by=False))\n",
    "\n",
    "print(portfolio.get_init_cash(group_by=group_by))\n",
    "print(portfolio_grouped.get_init_cash())\n",
    "print(portfolio_shared.get_init_cash())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53.9 µs ± 387 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "53.6 µs ± 187 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "870 µs ± 2.61 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "912 µs ± 2.62 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "57.9 µs ± 1.76 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "1.26 ms ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.get_init_cash()\n",
    "%timeit big_portfolio_grouped.get_init_cash(group_by=False)\n",
    "%timeit big_portfolio_shared.get_init_cash(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.get_init_cash(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.get_init_cash()\n",
    "%timeit big_portfolio_shared.get_init_cash()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43.2 µs ± 125 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "42.9 µs ± 611 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "533 µs ± 13.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "737 µs ± 44.3 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "46.6 µs ± 617 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "850 µs ± 37.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.get_init_cash()\n",
    "%timeit big_portfolio_grouped.get_init_cash(group_by=False)\n",
    "%timeit big_portfolio_shared.get_init_cash(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.get_init_cash(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.get_init_cash()\n",
    "%timeit big_portfolio_shared.get_init_cash()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    14000.0\n",
      "b    12000.0\n",
      "c    10000.0\n",
      "Name: init_cash, dtype: float64\n",
      "group\n",
      "first     26000.0\n",
      "second    10000.0\n",
      "Name: init_cash, dtype: float64\n",
      "group\n",
      "first     26000.0\n",
      "second    10000.0\n",
      "Name: init_cash, dtype: float64\n",
      "a    14000.0\n",
      "b    14000.0\n",
      "c    14000.0\n",
      "Name: init_cash, dtype: float64\n",
      "group\n",
      "first     26000.0\n",
      "second    26000.0\n",
      "Name: init_cash, dtype: float64\n",
      "group\n",
      "first     26000.0\n",
      "second    26000.0\n",
      "Name: init_cash, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(vbt.Portfolio.from_orders(\n",
    "    price_na, 1000., init_cash=InitCashMode.Auto, group_by=None).init_cash)\n",
    "\n",
    "print(vbt.Portfolio.from_orders(\n",
    "    price_na, 1000., init_cash=InitCashMode.Auto, group_by=group_by).init_cash)\n",
    "\n",
    "print(vbt.Portfolio.from_orders(\n",
    "    price_na, 1000., init_cash=InitCashMode.Auto, group_by=group_by, cash_sharing=True).init_cash)\n",
    "\n",
    "print(vbt.Portfolio.from_orders(\n",
    "    price_na, 1000., init_cash=InitCashMode.AutoAlign, group_by=None).init_cash)\n",
    "\n",
    "print(vbt.Portfolio.from_orders(\n",
    "    price_na, 1000., init_cash=InitCashMode.AutoAlign, group_by=group_by).init_cash)\n",
    "\n",
    "print(vbt.Portfolio.from_orders(\n",
    "    price_na, 1000., init_cash=InitCashMode.AutoAlign, group_by=group_by, cash_sharing=True).init_cash)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    a         b          c\n",
      "2020-01-01  100.00000  98.90010   98.87990\n",
      "2020-01-02   99.69598  98.60012   98.57588\n",
      "2020-01-03   99.89001  98.60012  101.41618\n",
      "2020-01-04   99.89001  98.30808  101.70822\n",
      "2020-01-05   94.68951  93.20858  101.70822\n",
      "                    a          b          c\n",
      "2020-01-01  100.00000  100.88010   98.87990\n",
      "2020-01-02   99.69598  100.97612   98.57588\n",
      "2020-01-03   99.89001  100.97612  101.41618\n",
      "2020-01-04   99.89001  100.46808  101.70822\n",
      "2020-01-05   94.68951  105.26858  101.70822\n",
      "                    a          b          c\n",
      "2020-01-01  100.00000  100.88010   98.87990\n",
      "2020-01-02   99.69598  100.97612   98.57588\n",
      "2020-01-03   99.89001  100.97612  101.41618\n",
      "2020-01-04   99.89001  100.46808  101.70822\n",
      "2020-01-05   94.68951  105.26858  101.70822\n",
      "                    a          b          c\n",
      "2020-01-01  200.00000  200.88010   98.87990\n",
      "2020-01-02  199.69598  200.97612   98.57588\n",
      "2020-01-03  199.89001  200.97612  101.41618\n",
      "2020-01-04  199.89001  200.46808  101.70822\n",
      "2020-01-05  194.68951  205.26858  101.70822\n",
      "                    a          b          c\n",
      "2020-01-01  200.88010  200.88010   98.87990\n",
      "2020-01-02  200.67210  200.97612   98.57588\n",
      "2020-01-03  200.86613  200.67210  101.41618\n",
      "2020-01-04  200.35809  200.35809  101.70822\n",
      "2020-01-05  199.95809  205.15859  101.70822\n",
      "group           first     second\n",
      "2020-01-01  200.88010   98.87990\n",
      "2020-01-02  200.67210   98.57588\n",
      "2020-01-03  200.86613  101.41618\n",
      "2020-01-04  200.35809  101.70822\n",
      "2020-01-05  199.95809  101.70822\n",
      "group           first     second\n",
      "2020-01-01  200.88010   98.87990\n",
      "2020-01-02  200.67210   98.57588\n",
      "2020-01-03  200.86613  101.41618\n",
      "2020-01-04  200.35809  101.70822\n",
      "2020-01-05  199.95809  101.70822\n",
      "group           first     second\n",
      "2020-01-01  200.88010   98.87990\n",
      "2020-01-02  200.67210   98.57588\n",
      "2020-01-03  200.86613  101.41618\n",
      "2020-01-04  200.35809  101.70822\n",
      "2020-01-05  199.95809  101.70822\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.cash(free=True))\n",
    "print(portfolio.cash())\n",
    "print(portfolio_grouped.cash(group_by=False))\n",
    "print(portfolio_shared.cash(group_by=False))\n",
    "print(portfolio_shared.cash(group_by=False, in_sim_order=True))\n",
    "\n",
    "print(portfolio.cash(group_by=group_by))\n",
    "print(portfolio_grouped.cash())\n",
    "print(portfolio_shared.cash())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.4 ms ± 920 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "18.3 ms ± 470 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "18.4 ms ± 2.12 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "17.6 ms ± 201 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "18.7 ms ± 303 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "18.3 ms ± 292 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "19.6 ms ± 1.75 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "21.7 ms ± 1.76 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.cash()\n",
    "%timeit big_portfolio.cash(free=True)\n",
    "%timeit big_portfolio_grouped.cash(group_by=False)\n",
    "%timeit big_portfolio_shared.cash(group_by=False)\n",
    "%timeit big_portfolio_shared.cash(group_by=False, in_sim_order=True)\n",
    "\n",
    "%timeit big_portfolio.cash(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.cash()\n",
    "%timeit big_portfolio_shared.cash()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              a    b    c\n",
      "2020-01-01  0.0  0.0  1.0\n",
      "2020-01-02  0.2  0.0  2.2\n",
      "2020-01-03  0.0  0.0  0.3\n",
      "2020-01-04  0.0  0.0  0.0\n",
      "2020-01-05  5.0  0.0  0.0\n",
      "              a     b    c\n",
      "2020-01-01  0.0   1.0  0.0\n",
      "2020-01-02  0.0   2.2  0.0\n",
      "2020-01-03  0.0   2.2  0.0\n",
      "2020-01-04  0.0   4.0  0.0\n",
      "2020-01-05  0.0  10.0  0.0\n",
      "              a     b    c\n",
      "2020-01-01  0.0  -1.0  1.0\n",
      "2020-01-02  0.2  -2.2  2.2\n",
      "2020-01-03  0.0  -2.2  0.3\n",
      "2020-01-04  0.0  -4.0  0.0\n",
      "2020-01-05  5.0 -10.0  0.0\n",
      "              a     b    c\n",
      "2020-01-01  0.0  -1.0  1.0\n",
      "2020-01-02  0.2  -2.2  2.2\n",
      "2020-01-03  0.0  -2.2  0.3\n",
      "2020-01-04  0.0  -4.0  0.0\n",
      "2020-01-05  5.0 -10.0  0.0\n",
      "              a     b    c\n",
      "2020-01-01  0.0  -1.0  1.0\n",
      "2020-01-02  0.2  -2.2  2.2\n",
      "2020-01-03  0.0  -2.2  0.3\n",
      "2020-01-04  0.0  -4.0  0.0\n",
      "2020-01-05  5.0 -10.0  0.0\n",
      "group       first  second\n",
      "2020-01-01   -1.0     1.0\n",
      "2020-01-02   -2.0     2.2\n",
      "2020-01-03   -2.2     0.3\n",
      "2020-01-04   -4.0     0.0\n",
      "2020-01-05   -5.0     0.0\n",
      "group       first  second\n",
      "2020-01-01   -1.0     1.0\n",
      "2020-01-02   -2.0     2.2\n",
      "2020-01-03   -2.2     0.3\n",
      "2020-01-04   -4.0     0.0\n",
      "2020-01-05   -5.0     0.0\n",
      "group       first  second\n",
      "2020-01-01   -1.0     1.0\n",
      "2020-01-02   -2.0     2.2\n",
      "2020-01-03   -2.2     0.3\n",
      "2020-01-04   -4.0     0.0\n",
      "2020-01-05   -5.0     0.0\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.asset_value(direction='longonly'))\n",
    "print(portfolio.asset_value(direction='shortonly'))\n",
    "\n",
    "print(portfolio.asset_value())\n",
    "print(portfolio_grouped.asset_value(group_by=False))\n",
    "print(portfolio_shared.asset_value(group_by=False))\n",
    "\n",
    "print(portfolio.asset_value(group_by=group_by))\n",
    "print(portfolio_grouped.asset_value())\n",
    "print(portfolio_shared.asset_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.7 ms ± 331 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "19.4 ms ± 2.51 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "18.4 ms ± 1.48 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "34.4 ms ± 308 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "33.6 ms ± 428 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "34.7 ms ± 213 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.asset_value()\n",
    "%timeit big_portfolio_grouped.asset_value(group_by=False)\n",
    "%timeit big_portfolio_shared.asset_value(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.asset_value(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.asset_value()\n",
    "%timeit big_portfolio_shared.asset_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   a    b         c\n",
      "2020-01-01  0.000000  0.0  0.010012\n",
      "2020-01-02  0.002002  0.0  0.021831\n",
      "2020-01-03  0.000000  0.0  0.002949\n",
      "2020-01-04  0.000000  0.0  0.000000\n",
      "2020-01-05  0.050156  0.0  0.000000\n",
      "              a         b    c\n",
      "2020-01-01  0.0  0.010010  0.0\n",
      "2020-01-02  0.0  0.021825  0.0\n",
      "2020-01-03  0.0  0.021825  0.0\n",
      "2020-01-04  0.0  0.039098  0.0\n",
      "2020-01-05  0.0  0.096891  0.0\n",
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.010214  0.010012\n",
      "2020-01-02  0.002002 -0.022822  0.021831\n",
      "2020-01-03  0.000000 -0.022822  0.002949\n",
      "2020-01-04  0.000000 -0.042414  0.000000\n",
      "2020-01-05  0.050156 -0.120180  0.000000\n",
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.010214  0.010012\n",
      "2020-01-02  0.002002 -0.022822  0.021831\n",
      "2020-01-03  0.000000 -0.022822  0.002949\n",
      "2020-01-04  0.000000 -0.042414  0.000000\n",
      "2020-01-05  0.050156 -0.120180  0.000000\n",
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.005053  0.010012\n",
      "2020-01-02  0.001001 -0.011202  0.021831\n",
      "2020-01-03  0.000000 -0.011202  0.002949\n",
      "2020-01-04  0.000000 -0.020586  0.000000\n",
      "2020-01-05  0.025039 -0.054583  0.000000\n",
      "group          first    second\n",
      "2020-01-01 -0.005053  0.010012\n",
      "2020-01-02 -0.010189  0.021831\n",
      "2020-01-03 -0.011208  0.002949\n",
      "2020-01-04 -0.020598  0.000000\n",
      "2020-01-05 -0.027338  0.000000\n",
      "group          first    second\n",
      "2020-01-01 -0.005053  0.010012\n",
      "2020-01-02 -0.010189  0.021831\n",
      "2020-01-03 -0.011208  0.002949\n",
      "2020-01-04 -0.020598  0.000000\n",
      "2020-01-05 -0.027338  0.000000\n",
      "group          first    second\n",
      "2020-01-01 -0.005053  0.010012\n",
      "2020-01-02 -0.010189  0.021831\n",
      "2020-01-03 -0.011208  0.002949\n",
      "2020-01-04 -0.020598  0.000000\n",
      "2020-01-05 -0.027338  0.000000\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.gross_exposure(direction='longonly'))\n",
    "print(portfolio.gross_exposure(direction='shortonly'))\n",
    "\n",
    "print(portfolio.gross_exposure())\n",
    "print(portfolio_grouped.gross_exposure(group_by=False))\n",
    "print(portfolio_shared.gross_exposure(group_by=False))\n",
    "\n",
    "print(portfolio.gross_exposure(group_by=group_by))\n",
    "print(portfolio_grouped.gross_exposure())\n",
    "print(portfolio_shared.gross_exposure())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.6 ms ± 5.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "44.3 ms ± 1.64 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "44.2 ms ± 1.07 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "58.8 ms ± 2.64 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "55 ms ± 1.09 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "60.9 ms ± 1.87 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.gross_exposure()\n",
    "%timeit big_portfolio_grouped.gross_exposure(group_by=False)\n",
    "%timeit big_portfolio_shared.gross_exposure(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.gross_exposure(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.gross_exposure()\n",
    "%timeit big_portfolio_shared.gross_exposure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.010010  0.010012\n",
      "2020-01-02  0.002002 -0.021825  0.021831\n",
      "2020-01-03  0.000000 -0.021825  0.002949\n",
      "2020-01-04  0.000000 -0.039098  0.000000\n",
      "2020-01-05  0.050156 -0.096891  0.000000\n",
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.010010  0.010012\n",
      "2020-01-02  0.002002 -0.021825  0.021831\n",
      "2020-01-03  0.000000 -0.021825  0.002949\n",
      "2020-01-04  0.000000 -0.039098  0.000000\n",
      "2020-01-05  0.050156 -0.096891  0.000000\n",
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.005002  0.010012\n",
      "2020-01-02  0.001001 -0.010956  0.021831\n",
      "2020-01-03  0.000000 -0.010956  0.002949\n",
      "2020-01-04  0.000000 -0.019772  0.000000\n",
      "2020-01-05  0.025039 -0.049211  0.000000\n",
      "group          first    second\n",
      "2020-01-01 -0.005002  0.010012\n",
      "2020-01-02 -0.009965  0.021831\n",
      "2020-01-03 -0.010962  0.002949\n",
      "2020-01-04 -0.019783  0.000000\n",
      "2020-01-05 -0.024611  0.000000\n",
      "group          first    second\n",
      "2020-01-01 -0.005002  0.010012\n",
      "2020-01-02 -0.009965  0.021831\n",
      "2020-01-03 -0.010962  0.002949\n",
      "2020-01-04 -0.019783  0.000000\n",
      "2020-01-05 -0.024611  0.000000\n",
      "group          first    second\n",
      "2020-01-01 -0.005002  0.010012\n",
      "2020-01-02 -0.009965  0.021831\n",
      "2020-01-03 -0.010962  0.002949\n",
      "2020-01-04 -0.019783  0.000000\n",
      "2020-01-05 -0.024611  0.000000\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.net_exposure())\n",
    "print(portfolio_grouped.net_exposure(group_by=False))\n",
    "print(portfolio_shared.net_exposure(group_by=False))\n",
    "\n",
    "print(portfolio.net_exposure(group_by=group_by))\n",
    "print(portfolio_grouped.net_exposure())\n",
    "print(portfolio_shared.net_exposure())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.5 ms ± 1.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "90.4 ms ± 1.52 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "95.4 ms ± 4.26 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "129 ms ± 3.21 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "119 ms ± 1.51 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "127 ms ± 1.85 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.net_exposure()\n",
    "%timeit big_portfolio_grouped.net_exposure(group_by=False)\n",
    "%timeit big_portfolio_shared.net_exposure(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.net_exposure(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.net_exposure()\n",
    "%timeit big_portfolio_shared.net_exposure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    a         b          c\n",
      "2020-01-01  100.00000  99.88010   99.87990\n",
      "2020-01-02   99.89598  98.77612  100.77588\n",
      "2020-01-03   99.89001  98.77612  101.71618\n",
      "2020-01-04   99.89001  96.46808  101.70822\n",
      "2020-01-05   99.68951  95.26858  101.70822\n",
      "                    a         b          c\n",
      "2020-01-01  100.00000  99.88010   99.87990\n",
      "2020-01-02   99.89598  98.77612  100.77588\n",
      "2020-01-03   99.89001  98.77612  101.71618\n",
      "2020-01-04   99.89001  96.46808  101.70822\n",
      "2020-01-05   99.68951  95.26858  101.70822\n",
      "                    a          b          c\n",
      "2020-01-01  200.00000  199.88010   99.87990\n",
      "2020-01-02  199.89598  198.77612  100.77588\n",
      "2020-01-03  199.89001  198.77612  101.71618\n",
      "2020-01-04  199.89001  196.46808  101.70822\n",
      "2020-01-05  199.68951  195.26858  101.70822\n",
      "                    a          b          c\n",
      "2020-01-01  199.88010  199.88010   99.87990\n",
      "2020-01-02  198.67210  198.77612  100.77588\n",
      "2020-01-03  198.66613  198.67210  101.71618\n",
      "2020-01-04  196.35809  196.35809  101.70822\n",
      "2020-01-05  194.95809  195.15859  101.70822\n",
      "group           first     second\n",
      "2020-01-01  199.88010   99.87990\n",
      "2020-01-02  198.67210  100.77588\n",
      "2020-01-03  198.66613  101.71618\n",
      "2020-01-04  196.35809  101.70822\n",
      "2020-01-05  194.95809  101.70822\n",
      "group           first     second\n",
      "2020-01-01  199.88010   99.87990\n",
      "2020-01-02  198.67210  100.77588\n",
      "2020-01-03  198.66613  101.71618\n",
      "2020-01-04  196.35809  101.70822\n",
      "2020-01-05  194.95809  101.70822\n",
      "group           first     second\n",
      "2020-01-01  199.88010   99.87990\n",
      "2020-01-02  198.67210  100.77588\n",
      "2020-01-03  198.66613  101.71618\n",
      "2020-01-04  196.35809  101.70822\n",
      "2020-01-05  194.95809  101.70822\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.value())\n",
    "print(portfolio_grouped.value(group_by=False))\n",
    "print(portfolio_shared.value(group_by=False))\n",
    "print(portfolio_shared.value(group_by=False, in_sim_order=True))\n",
    "\n",
    "print(portfolio.value(group_by=group_by))\n",
    "print(portfolio_grouped.value())\n",
    "print(portfolio_shared.value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.9 ms ± 1.21 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "35 ms ± 210 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "35.8 ms ± 81.1 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "46 ms ± 152 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "53.9 ms ± 626 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "50.4 ms ± 722 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "55.3 ms ± 941 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.value()\n",
    "%timeit big_portfolio_grouped.value(group_by=False)\n",
    "%timeit big_portfolio_shared.value(group_by=False)\n",
    "%timeit big_portfolio_shared.value(group_by=False, in_sim_order=True)\n",
    "\n",
    "%timeit big_portfolio.value(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.value()\n",
    "%timeit big_portfolio_shared.value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a   -0.31049\n",
      "b   -4.73142\n",
      "c    1.70822\n",
      "Name: total_profit, dtype: float64\n",
      "a   -0.31049\n",
      "b   -4.73142\n",
      "c    1.70822\n",
      "Name: total_profit, dtype: float64\n",
      "a   -0.31049\n",
      "b   -4.73142\n",
      "c    1.70822\n",
      "Name: total_profit, dtype: float64\n",
      "group\n",
      "first    -5.04191\n",
      "second    1.70822\n",
      "Name: total_profit, dtype: float64\n",
      "group\n",
      "first    -5.04191\n",
      "second    1.70822\n",
      "Name: total_profit, dtype: float64\n",
      "group\n",
      "first    -5.04191\n",
      "second    1.70822\n",
      "Name: total_profit, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.total_profit())\n",
    "print(portfolio_grouped.total_profit(group_by=False))\n",
    "print(portfolio_shared.total_profit(group_by=False))\n",
    "\n",
    "print(portfolio.total_profit(group_by=group_by))\n",
    "print(portfolio_grouped.total_profit())\n",
    "print(portfolio_shared.total_profit())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.3 ms ± 79.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "12.5 ms ± 190 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "12.5 ms ± 225 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "13.2 ms ± 224 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "12.6 ms ± 176 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "13.4 ms ± 217 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.total_profit()\n",
    "%timeit big_portfolio_grouped.total_profit(group_by=False)\n",
    "%timeit big_portfolio_shared.total_profit(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.total_profit(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.total_profit()\n",
    "%timeit big_portfolio_shared.total_profit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a     99.68951\n",
      "b     95.26858\n",
      "c    101.70822\n",
      "Name: final_value, dtype: float64\n",
      "a     99.68951\n",
      "b     95.26858\n",
      "c    101.70822\n",
      "Name: final_value, dtype: float64\n",
      "a    199.68951\n",
      "b    195.26858\n",
      "c    101.70822\n",
      "Name: final_value, dtype: float64\n",
      "group\n",
      "first     194.95809\n",
      "second    101.70822\n",
      "Name: final_value, dtype: float64\n",
      "group\n",
      "first     194.95809\n",
      "second    101.70822\n",
      "Name: final_value, dtype: float64\n",
      "group\n",
      "first     194.95809\n",
      "second    101.70822\n",
      "Name: final_value, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.final_value())\n",
    "print(portfolio_grouped.final_value(group_by=False))\n",
    "print(portfolio_shared.final_value(group_by=False))\n",
    "\n",
    "print(portfolio.final_value(group_by=group_by))\n",
    "print(portfolio_grouped.final_value())\n",
    "print(portfolio_shared.final_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.3 ms ± 159 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "12.2 ms ± 122 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "12.9 ms ± 128 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "14.1 ms ± 280 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "12 ms ± 145 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "14.1 ms ± 231 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.final_value()\n",
    "%timeit big_portfolio_grouped.final_value(group_by=False)\n",
    "%timeit big_portfolio_shared.final_value(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.final_value(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.final_value()\n",
    "%timeit big_portfolio_shared.final_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a   -0.003105\n",
      "b   -0.047314\n",
      "c    0.017082\n",
      "Name: total_return, dtype: float64\n",
      "a   -0.003105\n",
      "b   -0.047314\n",
      "c    0.017082\n",
      "Name: total_return, dtype: float64\n",
      "a   -0.001552\n",
      "b   -0.023657\n",
      "c    0.017082\n",
      "Name: total_return, dtype: float64\n",
      "group\n",
      "first    -0.025210\n",
      "second    0.017082\n",
      "Name: total_return, dtype: float64\n",
      "group\n",
      "first    -0.025210\n",
      "second    0.017082\n",
      "Name: total_return, dtype: float64\n",
      "group\n",
      "first    -0.025210\n",
      "second    0.017082\n",
      "Name: total_return, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.total_return())\n",
    "print(portfolio_grouped.total_return(group_by=False))\n",
    "print(portfolio_shared.total_return(group_by=False))\n",
    "\n",
    "print(portfolio.total_return(group_by=group_by))\n",
    "print(portfolio_grouped.total_return())\n",
    "print(portfolio_shared.total_return())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.5 ms ± 200 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "12.4 ms ± 207 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "12.8 ms ± 151 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "14 ms ± 178 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "12.6 ms ± 199 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "14.2 ms ± 122 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.total_return()\n",
    "%timeit big_portfolio_grouped.total_return(group_by=False)\n",
    "%timeit big_portfolio_shared.total_return(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.total_return(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.total_return()\n",
    "%timeit big_portfolio_shared.total_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.001199 -0.001201\n",
      "2020-01-02 -0.001040 -0.011053  0.008971\n",
      "2020-01-03 -0.000060  0.000000  0.009331\n",
      "2020-01-04  0.000000 -0.023366 -0.000078\n",
      "2020-01-05 -0.002007 -0.012434  0.000000\n",
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.001199 -0.001201\n",
      "2020-01-02 -0.001040 -0.011053  0.008971\n",
      "2020-01-03 -0.000060  0.000000  0.009331\n",
      "2020-01-04  0.000000 -0.023366 -0.000078\n",
      "2020-01-05 -0.002007 -0.012434  0.000000\n",
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.000600 -0.001201\n",
      "2020-01-02 -0.000520 -0.005523  0.008971\n",
      "2020-01-03 -0.000030  0.000000  0.009331\n",
      "2020-01-04  0.000000 -0.011611 -0.000078\n",
      "2020-01-05 -0.001003 -0.006105  0.000000\n",
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.000600 -0.001201\n",
      "2020-01-02 -0.000523 -0.005523  0.008971\n",
      "2020-01-03 -0.000030  0.000000  0.009331\n",
      "2020-01-04  0.000000 -0.011618 -0.000078\n",
      "2020-01-05 -0.001027 -0.006109  0.000000\n",
      "group          first    second\n",
      "2020-01-01 -0.000600 -0.001201\n",
      "2020-01-02 -0.006044  0.008971\n",
      "2020-01-03 -0.000030  0.009331\n",
      "2020-01-04 -0.011618 -0.000078\n",
      "2020-01-05 -0.007130  0.000000\n",
      "group          first    second\n",
      "2020-01-01 -0.000600 -0.001201\n",
      "2020-01-02 -0.006044  0.008971\n",
      "2020-01-03 -0.000030  0.009331\n",
      "2020-01-04 -0.011618 -0.000078\n",
      "2020-01-05 -0.007130  0.000000\n",
      "group          first    second\n",
      "2020-01-01 -0.000600 -0.001201\n",
      "2020-01-02 -0.006044  0.008971\n",
      "2020-01-03 -0.000030  0.009331\n",
      "2020-01-04 -0.011618 -0.000078\n",
      "2020-01-05 -0.007130  0.000000\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.returns())\n",
    "print(portfolio_grouped.returns(group_by=False))\n",
    "print(portfolio_shared.returns(group_by=False))\n",
    "print(portfolio_shared.returns(group_by=False, in_sim_order=True))\n",
    "\n",
    "print(portfolio.returns(group_by=group_by))\n",
    "print(portfolio_grouped.returns())\n",
    "print(portfolio_shared.returns())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.5 ms ± 2.59 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "37.4 ms ± 313 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "39.4 ms ± 226 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "52.3 ms ± 149 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "55.6 ms ± 310 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "50.9 ms ± 276 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "63.5 ms ± 4.23 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.returns()\n",
    "%timeit big_portfolio_grouped.returns(group_by=False)\n",
    "%timeit big_portfolio_shared.returns(group_by=False)\n",
    "%timeit big_portfolio_shared.returns(group_by=False, in_sim_order=True)\n",
    "\n",
    "%timeit big_portfolio.returns(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.returns()\n",
    "%timeit big_portfolio_shared.returns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  a         b         c\n",
      "2020-01-01  0.00000      -inf      -inf\n",
      "2020-01-02     -inf -1.103980  0.895980\n",
      "2020-01-03 -0.02985  0.000000  0.427409\n",
      "2020-01-04  0.00000 -1.049109 -0.026533\n",
      "2020-01-05     -inf -0.299875  0.000000\n",
      "                  a         b         c\n",
      "2020-01-01  0.00000      -inf      -inf\n",
      "2020-01-02     -inf -1.103980  0.895980\n",
      "2020-01-03 -0.02985  0.000000  0.427409\n",
      "2020-01-04  0.00000 -1.049109 -0.026533\n",
      "2020-01-05     -inf -0.299875  0.000000\n",
      "                  a         b         c\n",
      "2020-01-01  0.00000      -inf      -inf\n",
      "2020-01-02     -inf -1.103980  0.895980\n",
      "2020-01-03 -0.02985  0.000000  0.427409\n",
      "2020-01-04  0.00000 -1.049109 -0.026533\n",
      "2020-01-05     -inf -0.299875  0.000000\n",
      "group          first    second\n",
      "2020-01-01      -inf      -inf\n",
      "2020-01-02 -1.208000  0.895980\n",
      "2020-01-03 -0.002985  0.427409\n",
      "2020-01-04 -1.049109 -0.026533\n",
      "2020-01-05 -0.350000  0.000000\n",
      "group          first    second\n",
      "2020-01-01      -inf      -inf\n",
      "2020-01-02 -1.208000  0.895980\n",
      "2020-01-03 -0.002985  0.427409\n",
      "2020-01-04 -1.049109 -0.026533\n",
      "2020-01-05 -0.350000  0.000000\n",
      "group          first    second\n",
      "2020-01-01      -inf      -inf\n",
      "2020-01-02 -1.208000  0.895980\n",
      "2020-01-03 -0.002985  0.427409\n",
      "2020-01-04 -1.049109 -0.026533\n",
      "2020-01-05 -0.350000  0.000000\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.active_returns())\n",
    "print(portfolio_grouped.active_returns(group_by=False))\n",
    "print(portfolio_shared.active_returns(group_by=False))\n",
    "\n",
    "print(portfolio.active_returns(group_by=group_by))\n",
    "print(portfolio_grouped.active_returns())\n",
    "print(portfolio_shared.active_returns())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40.2 ms ± 5.18 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "36.9 ms ± 235 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "37.4 ms ± 193 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "51.7 ms ± 195 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "49.8 ms ± 220 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "52.4 ms ± 264 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.active_returns()\n",
    "%timeit big_portfolio_grouped.active_returns(group_by=False)\n",
    "%timeit big_portfolio_shared.active_returns(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.active_returns(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.active_returns()\n",
    "%timeit big_portfolio_shared.active_returns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                a      b      c\n",
      "2020-01-01  100.0  100.0  100.0\n",
      "2020-01-02  100.0  200.0  200.0\n",
      "2020-01-03  150.0  200.0  300.0\n",
      "2020-01-04  200.0  400.0  400.0\n",
      "2020-01-05  250.0  500.0  400.0\n",
      "                a      b      c\n",
      "2020-01-01  100.0  100.0  100.0\n",
      "2020-01-02  100.0  200.0  200.0\n",
      "2020-01-03  150.0  200.0  300.0\n",
      "2020-01-04  200.0  400.0  400.0\n",
      "2020-01-05  250.0  500.0  400.0\n",
      "                a       b      c\n",
      "2020-01-01  200.0   200.0  100.0\n",
      "2020-01-02  200.0   400.0  200.0\n",
      "2020-01-03  300.0   400.0  300.0\n",
      "2020-01-04  400.0   800.0  400.0\n",
      "2020-01-05  500.0  1000.0  400.0\n",
      "group       first  second\n",
      "2020-01-01  200.0   100.0\n",
      "2020-01-02  300.0   200.0\n",
      "2020-01-03  350.0   300.0\n",
      "2020-01-04  600.0   400.0\n",
      "2020-01-05  750.0   400.0\n",
      "group       first  second\n",
      "2020-01-01  200.0   100.0\n",
      "2020-01-02  300.0   200.0\n",
      "2020-01-03  350.0   300.0\n",
      "2020-01-04  600.0   400.0\n",
      "2020-01-05  750.0   400.0\n",
      "group       first  second\n",
      "2020-01-01  200.0   100.0\n",
      "2020-01-02  300.0   200.0\n",
      "2020-01-03  350.0   300.0\n",
      "2020-01-04  600.0   400.0\n",
      "2020-01-05  750.0   400.0\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.market_value())\n",
    "print(portfolio_grouped.market_value(group_by=False))\n",
    "print(portfolio_shared.market_value(group_by=False))\n",
    "\n",
    "print(portfolio.market_value(group_by=group_by))\n",
    "print(portfolio_grouped.market_value())\n",
    "print(portfolio_shared.market_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "909 µs ± 51.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "967 µs ± 34.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "1.81 ms ± 111 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "4.86 ms ± 415 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "2.82 ms ± 35.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "5.2 ms ± 387 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.market_value()\n",
    "%timeit big_portfolio_grouped.market_value(group_by=False)\n",
    "%timeit big_portfolio_shared.market_value(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.market_value(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.market_value()\n",
    "%timeit big_portfolio_shared.market_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   a     b         c\n",
      "2020-01-01  0.000000  0.00  0.000000\n",
      "2020-01-02  0.000000  1.00  1.000000\n",
      "2020-01-03  0.500000  0.00  0.500000\n",
      "2020-01-04  0.333333  1.00  0.333333\n",
      "2020-01-05  0.250000  0.25  0.000000\n",
      "                   a     b         c\n",
      "2020-01-01  0.000000  0.00  0.000000\n",
      "2020-01-02  0.000000  1.00  1.000000\n",
      "2020-01-03  0.500000  0.00  0.500000\n",
      "2020-01-04  0.333333  1.00  0.333333\n",
      "2020-01-05  0.250000  0.25  0.000000\n",
      "                   a     b         c\n",
      "2020-01-01  0.000000  0.00  0.000000\n",
      "2020-01-02  0.000000  1.00  1.000000\n",
      "2020-01-03  0.500000  0.00  0.500000\n",
      "2020-01-04  0.333333  1.00  0.333333\n",
      "2020-01-05  0.250000  0.25  0.000000\n",
      "group          first    second\n",
      "2020-01-01  0.000000  0.000000\n",
      "2020-01-02  0.500000  1.000000\n",
      "2020-01-03  0.166667  0.500000\n",
      "2020-01-04  0.714286  0.333333\n",
      "2020-01-05  0.250000  0.000000\n",
      "group          first    second\n",
      "2020-01-01  0.000000  0.000000\n",
      "2020-01-02  0.500000  1.000000\n",
      "2020-01-03  0.166667  0.500000\n",
      "2020-01-04  0.714286  0.333333\n",
      "2020-01-05  0.250000  0.000000\n",
      "group          first    second\n",
      "2020-01-01  0.000000  0.000000\n",
      "2020-01-02  0.500000  1.000000\n",
      "2020-01-03  0.166667  0.500000\n",
      "2020-01-04  0.714286  0.333333\n",
      "2020-01-05  0.250000  0.000000\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.market_returns())\n",
    "print(portfolio_grouped.market_returns(group_by=False))\n",
    "print(portfolio_shared.market_returns(group_by=False))\n",
    "\n",
    "print(portfolio.market_returns(group_by=group_by))\n",
    "print(portfolio_grouped.market_returns())\n",
    "print(portfolio_shared.market_returns())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7 ms ± 67.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "3.72 ms ± 66.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "5.11 ms ± 5.82 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "7.2 ms ± 618 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "4.1 ms ± 52.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "7.28 ms ± 40.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.market_returns()\n",
    "%timeit big_portfolio_grouped.market_returns(group_by=False)\n",
    "%timeit big_portfolio_shared.market_returns(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.market_returns(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.market_returns()\n",
    "%timeit big_portfolio_shared.market_returns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1.5\n",
      "b    4.0\n",
      "c    3.0\n",
      "Name: total_market_return, dtype: float64\n",
      "a    1.5\n",
      "b    4.0\n",
      "c    3.0\n",
      "Name: total_market_return, dtype: float64\n",
      "a    1.5\n",
      "b    4.0\n",
      "c    3.0\n",
      "Name: total_market_return, dtype: float64\n",
      "group\n",
      "first     2.75\n",
      "second    3.00\n",
      "Name: total_market_return, dtype: float64\n",
      "group\n",
      "first     2.75\n",
      "second    3.00\n",
      "Name: total_market_return, dtype: float64\n",
      "group\n",
      "first     2.75\n",
      "second    3.00\n",
      "Name: total_market_return, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.total_market_return())\n",
    "print(portfolio_grouped.total_market_return(group_by=False))\n",
    "print(portfolio_shared.total_market_return(group_by=False))\n",
    "\n",
    "print(portfolio.total_market_return(group_by=group_by))\n",
    "print(portfolio_grouped.total_market_return())\n",
    "print(portfolio_shared.total_market_return())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "975 µs ± 61.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "937 µs ± 10.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "1.83 ms ± 7.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "4.77 ms ± 30 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "2.85 ms ± 11.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "5.21 ms ± 79.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit big_portfolio.total_market_return()\n",
    "%timeit big_portfolio_grouped.total_market_return(group_by=False)\n",
    "%timeit big_portfolio_shared.total_market_return(group_by=False)\n",
    "\n",
    "%timeit big_portfolio.total_market_return(group_by=big_group_by)\n",
    "%timeit big_portfolio_grouped.total_market_return()\n",
    "%timeit big_portfolio_shared.total_market_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "group          first    second\n",
      "2020-01-01 -0.000599 -0.001201\n",
      "2020-01-02 -0.006639  0.007759\n",
      "2020-01-03 -0.006669  0.017162\n",
      "2020-01-04 -0.018210  0.017082\n",
      "2020-01-05 -0.025210  0.017082\n",
      "                   a         b         c\n",
      "2020-01-01  0.000000 -0.000599 -0.001201\n",
      "2020-01-02 -0.000520 -0.006119  0.007759\n",
      "2020-01-03 -0.000550 -0.006119  0.017162\n",
      "2020-01-04 -0.000550 -0.017660  0.017082\n",
      "2020-01-05 -0.001552 -0.023657  0.017082\n"
     ]
    }
   ],
   "source": [
    "print(portfolio_shared.cumulative_returns())\n",
    "print(portfolio_shared.cumulative_returns(group_by=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "group\n",
      "first    -20.095907\n",
      "second    12.345065\n",
      "Name: sharpe_ratio, dtype: float64\n",
      "group\n",
      "first    -59.622588\n",
      "second   -23.917188\n",
      "Name: sharpe_ratio, dtype: float64\n",
      "group\n",
      "first    -16.697884\n",
      "second    10.257635\n",
      "Name: sharpe_ratio, dtype: float64\n",
      "a   -13.309506\n",
      "b   -19.278625\n",
      "c    12.345065\n",
      "Name: sharpe_ratio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(portfolio_shared.sharpe_ratio())\n",
    "print(portfolio_shared.sharpe_ratio(risk_free=0.01))\n",
    "print(portfolio_shared.sharpe_ratio(year_freq='252D'))\n",
    "print(portfolio_shared.sharpe_ratio(group_by=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start                         2020-01-01 00:00:00\n",
      "End                           2020-01-05 00:00:00\n",
      "Period                            5 days 00:00:00\n",
      "Start Value                                 100.0\n",
      "End Value                                98.88877\n",
      "Total Return [%]                         -1.11123\n",
      "Benchmark Return [%]                   283.333333\n",
      "Max Gross Exposure [%]                   2.059062\n",
      "Total Fees Paid                           0.42223\n",
      "Max Drawdown [%]                         1.645124\n",
      "Max Drawdown Duration             3 days 08:00:00\n",
      "Total Trades                                  2.0\n",
      "Total Closed Trades                      1.333333\n",
      "Total Open Trades                        0.666667\n",
      "Open Trade PnL                          -1.504206\n",
      "Win Rate [%]                            33.333333\n",
      "Best Trade [%]                         -98.380588\n",
      "Worst Trade [%]                       -100.803855\n",
      "Avg Winning Trade [%]                  143.916254\n",
      "Avg Losing Trade [%]                   -221.34646\n",
      "Avg Winning Trade Duration        2 days 12:00:00\n",
      "Avg Losing Trade Duration         2 days 00:00:00\n",
      "Profit Factor                                 inf\n",
      "Expectancy                               0.108273\n",
      "Sharpe Ratio                            -6.751008\n",
      "Calmar Ratio                         10378.930331\n",
      "Omega Ratio                                4.7687\n",
      "Sortino Ratio                           31.599761\n",
      "Name: agg_func_mean, dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/olegpolakow/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning: Object has multiple columns. Aggregating using <function mean at 0x7ff110b83620>. Pass column to select a single column/group.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.stats())  # mean statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start                         2020-01-01 00:00:00\n",
      "End                           2020-01-05 00:00:00\n",
      "Period                            5 days 00:00:00\n",
      "Start Value                                 100.0\n",
      "End Value                                99.68951\n",
      "Total Return [%]                         -0.31049\n",
      "Benchmark Return [%]                        150.0\n",
      "Max Gross Exposure [%]                   5.015573\n",
      "Total Fees Paid                           0.35549\n",
      "Max Drawdown [%]                          0.31049\n",
      "Max Drawdown Duration             4 days 00:00:00\n",
      "Total Trades                                    2\n",
      "Total Closed Trades                             1\n",
      "Total Open Trades                               1\n",
      "Open Trade PnL                            -0.2005\n",
      "Win Rate [%]                                  0.0\n",
      "Best Trade [%]                         -54.450495\n",
      "Worst Trade [%]                        -54.450495\n",
      "Avg Winning Trade [%]                         NaN\n",
      "Avg Losing Trade [%]                   -54.450495\n",
      "Avg Winning Trade Duration                    NaT\n",
      "Avg Losing Trade Duration         1 days 00:00:00\n",
      "Profit Factor                                 0.0\n",
      "Expectancy                               -0.10999\n",
      "Sharpe Ratio                           -13.308045\n",
      "Calmar Ratio                           -65.408686\n",
      "Omega Ratio                                   0.0\n",
      "Sortino Ratio                          -11.738865\n",
      "Name: a, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(portfolio['a'].stats())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start                         2020-01-01 00:00:00\n",
      "End                           2020-01-05 00:00:00\n",
      "Period                            5 days 00:00:00\n",
      "Start Value                                 100.0\n",
      "End Value                                99.68951\n",
      "Total Return [%]                         -0.31049\n",
      "Benchmark Return [%]                        150.0\n",
      "Max Gross Exposure [%]                   5.015573\n",
      "Total Fees Paid                           0.35549\n",
      "Max Drawdown [%]                          0.31049\n",
      "Max Drawdown Duration             4 days 00:00:00\n",
      "Total Trades                                    2\n",
      "Total Closed Trades                             1\n",
      "Total Open Trades                               1\n",
      "Open Trade PnL                            -0.2005\n",
      "Win Rate [%]                                  0.0\n",
      "Best Trade [%]                         -54.450495\n",
      "Worst Trade [%]                        -54.450495\n",
      "Avg Winning Trade [%]                         NaN\n",
      "Avg Losing Trade [%]                   -54.450495\n",
      "Avg Winning Trade Duration                    NaT\n",
      "Avg Losing Trade Duration         1 days 00:00:00\n",
      "Profit Factor                                 0.0\n",
      "Expectancy                               -0.10999\n",
      "Sharpe Ratio                          -227.458628\n",
      "Calmar Ratio                           -65.408686\n",
      "Omega Ratio                                   0.0\n",
      "Sortino Ratio                          -19.104372\n",
      "Name: a, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(portfolio['a'].stats(settings=dict(required_return=0.1, risk_free=0.01)))  # test kwargs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start                         2020-01-01 00:00:00\n",
      "End                           2020-01-05 00:00:00\n",
      "Period                            5 days 00:00:00\n",
      "Start Value                                 100.0\n",
      "End Value                                99.68951\n",
      "Total Return [%]                         -0.31049\n",
      "Benchmark Return [%]                        150.0\n",
      "Max Gross Exposure [%]                   5.015573\n",
      "Total Fees Paid                           0.35549\n",
      "Max Drawdown [%]                          0.31049\n",
      "Max Drawdown Duration             4 days 00:00:00\n",
      "Total Trades                                    2\n",
      "Total Closed Trades                             1\n",
      "Total Open Trades                               1\n",
      "Open Trade PnL                            -0.2005\n",
      "Win Rate [%]                                  0.0\n",
      "Best Trade [%]                          -3.970297\n",
      "Worst Trade [%]                        -54.450495\n",
      "Avg Winning Trade [%]                         NaN\n",
      "Avg Losing Trade [%]                   -29.210396\n",
      "Avg Winning Trade Duration                    NaT\n",
      "Avg Losing Trade Duration         1 days 00:00:00\n",
      "Profit Factor                                 0.0\n",
      "Expectancy                              -0.155245\n",
      "Sharpe Ratio                           -13.308045\n",
      "Calmar Ratio                           -65.408686\n",
      "Omega Ratio                                   0.0\n",
      "Sortino Ratio                          -11.738865\n",
      "Name: a, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(portfolio['a'].stats(settings=dict(incl_open=True)))  # test incl_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start                         2020-01-01 00:00:00\n",
       "End                           2020-01-05 00:00:00\n",
       "Period                            5 days 00:00:00\n",
       "Start Value                                 200.0\n",
       "End Value                               194.95809\n",
       "Total Return [%]                        -2.520955\n",
       "Benchmark Return [%]                        275.0\n",
       "Max Gross Exposure [%]                  -0.505305\n",
       "Total Fees Paid                           0.82091\n",
       "Max Drawdown [%]                         2.462481\n",
       "Max Drawdown Duration             4 days 00:00:00\n",
       "Total Trades                                    4\n",
       "Total Closed Trades                             2\n",
       "Total Open Trades                               2\n",
       "Open Trade PnL                          -4.512618\n",
       "Win Rate [%]                                  0.0\n",
       "Best Trade [%]                         -54.450495\n",
       "Worst Trade [%]                       -388.242424\n",
       "Avg Winning Trade [%]                         NaN\n",
       "Avg Losing Trade [%]                   -221.34646\n",
       "Avg Winning Trade Duration                    NaT\n",
       "Avg Losing Trade Duration         2 days 00:00:00\n",
       "Profit Factor                                 0.0\n",
       "Expectancy                              -0.264646\n",
       "Sharpe Ratio                           -20.095907\n",
       "Calmar Ratio                           -34.312217\n",
       "Omega Ratio                                   0.0\n",
       "Sortino Ratio                          -14.554512\n",
       "Name: first, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_grouped['first'].stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.testing.assert_series_equal(portfolio['c'].stats(), portfolio.stats(column='c'))\n",
    "pd.testing.assert_series_equal(portfolio['c'].stats(), portfolio_grouped.stats(column='c', group_by=False))\n",
    "pd.testing.assert_series_equal(portfolio_grouped['second'].stats(), portfolio_grouped.stats(column='second'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64.9 ms ± 525 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "76 ms ± 478 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "145 ms ± 588 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "101 ms ± 87.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "72.4 ms ± 151 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "158 ms ± 1.02 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Calculates stats for one column\n",
    "%timeit big_portfolio.iloc[0].stats()\n",
    "%timeit big_portfolio_grouped.iloc[0].stats(group_by=False, column=0)\n",
    "%timeit big_portfolio_shared.iloc[0].stats(group_by=False, column=0)\n",
    "\n",
    "%timeit big_portfolio.iloc[0].stats(group_by=np.array([0]))\n",
    "%timeit big_portfolio_grouped.iloc[0].stats()\n",
    "%timeit big_portfolio_shared.iloc[0].stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_portfolio_one = big_portfolio.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start\n",
      "1.72 ms ± 643 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "end\n",
      "1.17 ms ± 25.3 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "period\n",
      "1.18 ms ± 33.2 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "start_value\n",
      "1.23 ms ± 51.1 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "end_value\n",
      "2.43 ms ± 126 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "total_return\n",
      "2.38 ms ± 7.53 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "benchmark_return\n",
      "1.56 ms ± 12.2 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "max_gross_exposure\n",
      "5.51 ms ± 22.4 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "total_fees_paid\n",
      "2.11 ms ± 132 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "max_dd\n",
      "5.3 ms ± 22.5 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "max_dd_duration\n",
      "5.39 ms ± 49.2 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "total_trades\n",
      "2.68 ms ± 320 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "total_closed_trades\n",
      "4.07 ms ± 111 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "total_open_trades\n",
      "3.24 ms ± 28.8 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "open_trade_pnl\n",
      "3.72 ms ± 136 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "win_rate\n",
      "4.19 ms ± 128 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "best_trade\n",
      "3.62 ms ± 44.6 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "worst_trade\n",
      "3.71 ms ± 178 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "avg_winning_trade\n",
      "4.54 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "avg_losing_trade\n",
      "4.46 ms ± 122 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "avg_winning_trade_duration\n",
      "4.5 ms ± 140 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "avg_losing_trade_duration\n",
      "4.99 ms ± 224 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "profit_factor\n",
      "5.82 ms ± 86.1 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "expectancy\n",
      "7.09 ms ± 288 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "sharpe_ratio\n",
      "8.79 ms ± 613 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "calmar_ratio\n",
      "8.56 ms ± 527 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "omega_ratio\n",
      "8.38 ms ± 215 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "sortino_ratio\n",
      "8.15 ms ± 101 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "for metric in big_portfolio_one.metrics:\n",
    "    print(metric)\n",
    "    %timeit -n 10 big_portfolio_one.stats(metrics=metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start\n",
      "1.51 ms ± 362 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "end\n",
      "1.54 ms ± 74 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "period\n",
      "1.92 ms ± 19.8 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "start_value\n",
      "2.46 ms ± 117 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "end_value\n",
      "4.1 ms ± 117 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "total_return\n",
      "5.67 ms ± 12.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "benchmark_return\n",
      "6.59 ms ± 46.7 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "max_gross_exposure\n",
      "11.4 ms ± 33.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "total_fees_paid\n",
      "12.8 ms ± 232 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "max_dd\n",
      "17.6 ms ± 281 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "max_dd_duration\n",
      "19.1 ms ± 833 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "total_trades\n",
      "20.6 ms ± 434 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "total_closed_trades\n",
      "22.5 ms ± 234 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "total_open_trades\n",
      "23.9 ms ± 228 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "open_trade_pnl\n",
      "25.7 ms ± 140 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "win_rate\n",
      "28 ms ± 246 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "best_trade\n",
      "30.3 ms ± 710 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "worst_trade\n",
      "31.9 ms ± 606 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "avg_winning_trade\n",
      "35.8 ms ± 1.27 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "avg_losing_trade\n",
      "36.8 ms ± 183 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "avg_winning_trade_duration\n",
      "39.5 ms ± 140 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "avg_losing_trade_duration\n",
      "41.7 ms ± 235 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "profit_factor\n",
      "45.8 ms ± 240 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "expectancy\n",
      "50.6 ms ± 425 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "sharpe_ratio\n",
      "58 ms ± 176 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "calmar_ratio\n",
      "58.5 ms ± 182 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "omega_ratio\n",
      "59.1 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "sortino_ratio\n",
      "60 ms ± 144 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "metrics = []\n",
    "for metric in big_portfolio_one.metrics:\n",
    "    print(metric)\n",
    "    metrics.append(metric)\n",
    "    %timeit -n 10 big_portfolio_one.stats(metrics=metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38 ms ± 179 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "47.3 ms ± 852 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "94.6 ms ± 237 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "68.4 ms ± 511 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "40.1 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "90.3 ms ± 1.48 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Calculates stats for one column with caching enabled\n",
    "vbt.settings.caching.enabled = True\n",
    "%timeit big_portfolio.iloc[0].stats()\n",
    "%timeit big_portfolio_grouped.iloc[0].stats(group_by=False, column=0)\n",
    "%timeit big_portfolio_shared.iloc[0].stats(group_by=False, column=0)\n",
    "\n",
    "%timeit big_portfolio.iloc[0].stats(group_by=np.array([0]))\n",
    "%timeit big_portfolio_grouped.iloc[0].stats()\n",
    "%timeit big_portfolio_shared.iloc[0].stats()\n",
    "vbt.settings.caching.enabled = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "560 ms ± 29.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "554 ms ± 13.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "609 ms ± 4.04 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "676 ms ± 10.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "635 ms ± 12.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "741 ms ± 14.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# Calculates stats for all columns and selects one, takes advantage of caching (which is disabled here)\n",
    "# Thus some series are re-calculated multiple times, such as records and returns\n",
    "%timeit big_portfolio.stats(column=0)\n",
    "%timeit big_portfolio_grouped.stats(group_by=False, column=0)\n",
    "%timeit big_portfolio_shared.stats(group_by=False, column=0)\n",
    "\n",
    "%timeit big_portfolio.stats(group_by=big_group_by, column=0)\n",
    "%timeit big_portfolio_grouped.stats(column=0)\n",
    "%timeit big_portfolio_shared.stats(column=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start                        2020-01-01 00:00:00\n",
      "End                          2020-01-05 00:00:00\n",
      "Period                           5 days 00:00:00\n",
      "Total Return [%]                        -1.11123\n",
      "Benchmark Return [%]                  283.333333\n",
      "Annualized Return [%]                  42.313666\n",
      "Annualized Volatility [%]               9.984895\n",
      "Max Drawdown [%]                        1.645124\n",
      "Max Drawdown Duration            3 days 08:00:00\n",
      "Sharpe Ratio                           -6.751008\n",
      "Calmar Ratio                        10378.930331\n",
      "Omega Ratio                               4.7687\n",
      "Sortino Ratio                          31.599761\n",
      "Skew                                   -0.399797\n",
      "Kurtosis                               -1.202541\n",
      "Tail Ratio                              3.164402\n",
      "Common Sense Ratio                     10.883528\n",
      "Value at Risk                           -0.00799\n",
      "Alpha                                   -0.35365\n",
      "Beta                                   -0.001234\n",
      "Name: agg_func_mean, dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/portfolio/base.py:4618: UserWarning: Object has multiple columns. Aggregating using <function mean at 0x7ffd1144a620>. Pass column to select a single column/group.\n",
      "  return getattr(returns_acc, 'stats')(settings=settings, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "print(portfolio.returns_stats())  # mean statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start                        2020-01-01 00:00:00\n",
      "End                          2020-01-05 00:00:00\n",
      "Period                           5 days 00:00:00\n",
      "Total Return [%]                        -0.31049\n",
      "Benchmark Return [%]                       150.0\n",
      "Annualized Return [%]                 -20.308743\n",
      "Annualized Volatility [%]               1.704408\n",
      "Max Drawdown [%]                         0.31049\n",
      "Max Drawdown Duration            4 days 00:00:00\n",
      "Sharpe Ratio                          -13.308045\n",
      "Calmar Ratio                          -65.408686\n",
      "Omega Ratio                                  0.0\n",
      "Sortino Ratio                         -11.738865\n",
      "Skew                                   -1.219107\n",
      "Kurtosis                                0.122976\n",
      "Tail Ratio                                   0.0\n",
      "Common Sense Ratio                           0.0\n",
      "Value at Risk                          -0.001814\n",
      "Alpha                                  -0.248883\n",
      "Beta                                    0.000749\n",
      "Name: a, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(portfolio['a'].returns_stats())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start                        2020-01-01 00:00:00\n",
      "End                          2020-01-05 00:00:00\n",
      "Period                           5 days 00:00:00\n",
      "Total Return [%]                       -2.520955\n",
      "Benchmark Return [%]                       275.0\n",
      "Annualized Return [%]                 -84.493192\n",
      "Annualized Volatility [%]               9.234269\n",
      "Max Drawdown [%]                        2.462481\n",
      "Max Drawdown Duration            4 days 00:00:00\n",
      "Sharpe Ratio                          -20.095907\n",
      "Calmar Ratio                          -34.312217\n",
      "Omega Ratio                                  0.0\n",
      "Sortino Ratio                         -14.554512\n",
      "Skew                                   -0.254782\n",
      "Kurtosis                               -1.363876\n",
      "Tail Ratio                              0.013427\n",
      "Common Sense Ratio                      0.002082\n",
      "Value at Risk                           -0.01072\n",
      "Alpha                                  -0.053947\n",
      "Beta                                   -0.015121\n",
      "Name: first, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(portfolio_grouped['first'].returns_stats())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.testing.assert_series_equal(\n",
    "    portfolio['c'].returns_stats(), \n",
    "    portfolio.returns_stats(column='c'))\n",
    "pd.testing.assert_series_equal(\n",
    "    portfolio['c'].returns_stats(), \n",
    "    portfolio_grouped.returns_stats(column='c', group_by=False))\n",
    "pd.testing.assert_series_equal(\n",
    "    portfolio_grouped['second'].returns_stats(), \n",
    "    portfolio_grouped.returns_stats(column='second'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><path class=\"point\" transform=\"translate(450.31,232.96)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><path class=\"point\" transform=\"translate(588.75,232.96)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"text\"/></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"x3tick\"><text text-anchor=\"middle\" x=\"0\" y=\"905\" transform=\"translate(130,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\"><tspan class=\"line\" dy=\"0em\" x=\"0\" y=\"905\">Jan 1</tspan><tspan class=\"line\" dy=\"1.3em\" x=\"0\" y=\"905\">2020</tspan></text></g><g class=\"x3tick\"><text text-anchor=\"middle\" x=\"0\" y=\"905\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; 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font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Close</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(31, 119, 180); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"74.859375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(77.359375,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Buy</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M-4.62,2H4.62L0,-4Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(55, 177, 63); fill-opacity: 1; stroke: rgb(38, 123, 44); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"65.421875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(145.28125,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Sell</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M-4.62,-2H4.62L0,4Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(234, 67, 53); fill-opacity: 1; stroke: rgb(181, 31, 18); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"64.4375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(212.21875,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Closed - Loss</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M7,0A7,7 0 1,1 0,-7A7,7 0 0,1 7,0Z\" style=\"opacity: 0.9; stroke-width: 1px; fill: rgb(234, 67, 53); fill-opacity: 1; stroke: rgb(181, 31, 18); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"122.6875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(337.40625,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; 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stroke: rgb(127, 127, 127); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(127, 127, 127); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"110.421875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(527,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Value</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(148, 103, 189); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; 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      "/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/generic/plots_builder.py:340: UserWarning:\n",
      "\n",
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      "\n",
      "/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/generic/plots_builder.py:340: UserWarning:\n",
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   "source": [
    "portfolio_shared['first'].plot().show_svg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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transform=\"translate(173.6,230.46)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><path class=\"point\" transform=\"translate(311.98,230.46)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><path class=\"point\" transform=\"translate(450.37,230.46)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><path class=\"point\" transform=\"translate(588.75,230.46)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"text\"/></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"x14tick\"><text text-anchor=\"middle\" x=\"0\" y=\"4095\" transform=\"translate(130.21,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\"><tspan class=\"line\" dy=\"0em\" x=\"0\" y=\"4095\">Jan 1</tspan><tspan class=\"line\" dy=\"1.3em\" x=\"0\" y=\"4095\">2020</tspan></text></g><g class=\"x14tick\"><text text-anchor=\"middle\" x=\"0\" y=\"4095\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(268.6,0)\">Jan 2</text></g><g class=\"x14tick\"><text text-anchor=\"middle\" x=\"0\" y=\"4095\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(406.98,0)\">Jan 3</text></g><g class=\"x14tick\"><text text-anchor=\"middle\" x=\"0\" y=\"4095\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(545.37,0)\">Jan 4</text></g><g class=\"x14tick\"><text text-anchor=\"middle\" x=\"0\" y=\"4095\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(683.75,0)\">Jan 5</text></g></g><g class=\"yaxislayer-above\"><g class=\"y14tick\"><text text-anchor=\"end\" x=\"94\" y=\"4.199999999999999\" transform=\"translate(0,4065.919896729776)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\">0</text></g><g class=\"y14tick\"><text text-anchor=\"end\" x=\"94\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 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y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,3852.199896729776)\">0.05</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"iciclelayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-fa6231\"><g class=\"clips\"/><clipPath id=\"legendfa6231\"><rect width=\"609\" height=\"86\" x=\"0\" y=\"0\"/></clipPath></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"><path data-index=\"2\" fill-rule=\"evenodd\" d=\"M95,706.1476764199653L720,706.1476764199653\" clip-path=\"url(#clipfa6231y3)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; 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2px;\"/><path data-index=\"11\" fill-rule=\"evenodd\" d=\"M95,3267.3122203098105L720,3267.3122203098105\" clip-path=\"url(#clipfa6231y12)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"12\" fill-rule=\"evenodd\" d=\"M95,3566.966058519793L720,3566.966058519793\" clip-path=\"url(#clipfa6231y13)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"13\" fill-rule=\"evenodd\" d=\"M95,4065.919896729776L720,4065.919896729776\" clip-path=\"url(#clipfa6231y14)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/></g></g><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(111,11.966265060240659)\"><rect 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stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"74.859375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(125.1875,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Buy</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M-4.62,2H4.62L0,-4Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(55, 177, 63); fill-opacity: 1; stroke: rgb(38, 123, 44); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"65.421875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(250.375,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Sell</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M-4.62,-2H4.62L0,4Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(234, 67, 53); fill-opacity: 1; stroke: rgb(181, 31, 18); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"64.4375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(375.5625,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Entry</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3.5,3.5H-3.5V-3.5H3.5Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(66, 133, 244); fill-opacity: 1; stroke: rgb(11, 84, 205); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"74.640625\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(500.75,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Exit - Loss</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3.5,3.5H-3.5V-3.5H3.5Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(234, 67, 53); fill-opacity: 1; stroke: rgb(181, 31, 18); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"105.578125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Active</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3.5,3.5H-3.5V-3.5H3.5Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(255, 170, 0); fill-opacity: 1; stroke: rgb(178, 118, 0); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"79.234375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(125.1875,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Closed - Loss</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M7,0A7,7 0 1,1 0,-7A7,7 0 0,1 7,0Z\" style=\"opacity: 0.9; stroke-width: 1px; fill: rgb(234, 67, 53); fill-opacity: 1; stroke: rgb(181, 31, 18); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"122.6875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(250.375,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Open</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3.5,0A3.5,3.5 0 1,1 0,-3.5A3.5,3.5 0 0,1 3.5,0Z\" style=\"opacity: 0.75; stroke-width: 1px; fill: rgb(255, 170, 0); fill-opacity: 1; stroke: rgb(178, 118, 0); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"74.171875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(375.5625,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Assets</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(140, 86, 75); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(140, 86, 75); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"81.34375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(500.75,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Cash</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(44, 160, 44); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(44, 160, 44); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"71.9375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Asset Value</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(23, 190, 207); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(23, 190, 207); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"112.75\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(125.1875,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Value</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(148, 103, 189); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(148, 103, 189); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"75.953125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(250.375,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Benchmark</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(127, 127, 127); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(127, 127, 127); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"110.421875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(375.5625,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Peak</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M4.55,0L0,4.55L-4.55,0L0,-4.55Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(66, 133, 244); fill-opacity: 1; stroke: rgb(11, 84, 205); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"71.203125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(500.75,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Active</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M4.55,0L0,4.55L-4.55,0L0,-4.55Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(255, 170, 0); fill-opacity: 1; stroke: rgb(178, 118, 0); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"79.234375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,71.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Drawdown</text><g class=\"layers\"><g class=\"legendfill\"><path class=\"js-fill\" d=\"M5,0h30v6h-30z\" style=\"stroke-width: 0; fill: rgb(220, 57, 18); fill-opacity: 0.3;\"/></g><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(220, 57, 18); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(220, 57, 18); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"106.078125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(125.1875,71.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Exposure</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(227, 119, 194); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(227, 119, 194); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"98.078125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g></g></g><rect class=\"scrollbar\" rx=\"20\" ry=\"3\" width=\"0\" height=\"0\" style=\"fill: rgb(128, 139, 164); fill-opacity: 1;\" x=\"0\" y=\"0\"/></g><g class=\"g-gtitle\"/><g class=\"g-xtitle\"/><g class=\"g-x2title\"/><g class=\"g-x3title\"/><g class=\"g-x4title\"/><g class=\"g-x5title\"/><g class=\"g-x6title\"/><g class=\"g-x7title\"/><g class=\"g-x8title\"/><g class=\"g-x9title\"/><g class=\"g-x10title\"/><g class=\"g-x11title\"/><g class=\"g-x12title\"/><g class=\"g-x13title\"/><g class=\"g-x14title\"><text class=\"x14title\" x=\"407.5\" y=\"4137.909375\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Index</text></g><g class=\"g-ytitle\"><text class=\"ytitle\" transform=\"rotate(-90,49.575,259.2700516351119)\" x=\"49.575\" y=\"259.2700516351119\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Price</text></g><g class=\"g-y2title\"><text class=\"y2title\" transform=\"rotate(-90,49.575,543.8438898450945)\" x=\"49.575\" y=\"543.8438898450945\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Price</text></g><g class=\"g-y3title\" transform=\"translate(2.4365234375,0)\"><text class=\"y3title\" transform=\"rotate(-90,11.575000000000003,828.4177280550771)\" x=\"11.575000000000003\" y=\"828.4177280550771\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Trade PnL</text></g><g class=\"g-y4title\"><text class=\"y4title\" transform=\"rotate(-90,49.575,1112.99156626506)\" x=\"49.575\" y=\"1112.99156626506\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Asset flow</text></g><g class=\"g-y5title\"><text class=\"y5title\" transform=\"rotate(-90,51.746875,1397.565404475043)\" x=\"51.746875\" y=\"1397.565404475043\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Cash flow</text></g><g class=\"g-y6title\"><text class=\"y6title\" transform=\"rotate(-90,49.575,1682.139242685026)\" x=\"49.575\" y=\"1682.139242685026\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Assets</text></g><g class=\"g-y7title\"><text class=\"y7title\" transform=\"rotate(-90,46.309375,1966.7130808950085)\" x=\"46.309375\" y=\"1966.7130808950085\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Cash</text></g><g class=\"g-y8title\"><text class=\"y8title\" transform=\"rotate(-90,61.559375,2251.286919104991)\" x=\"61.559375\" y=\"2251.286919104991\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Asset value</text></g><g class=\"g-y9title\"><text class=\"y9title\" transform=\"rotate(-90,34.309375,2535.8607573149743)\" x=\"34.309375\" y=\"2535.8607573149743\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Value</text></g><g class=\"g-y10title\"><text class=\"y10title\" transform=\"rotate(-90,49.575,2820.4345955249573)\" x=\"49.575\" y=\"2820.4345955249573\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Cumulative returns</text></g><g class=\"g-y11title\"><text class=\"y11title\" transform=\"rotate(-90,41.934375,3105.00843373494)\" x=\"41.934375\" y=\"3105.00843373494\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Value</text></g><g class=\"g-y12title\"><text class=\"y12title\" transform=\"rotate(-90,38.825,3389.5822719449225)\" x=\"38.825\" y=\"3389.5822719449225\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Drawdown</text></g><g class=\"g-y13title\"><text class=\"y13title\" transform=\"rotate(-90,49.575,3674.156110154905)\" x=\"49.575\" y=\"3674.156110154905\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Gross exposure</text></g><g class=\"g-y14title\"><text class=\"y14title\" transform=\"rotate(-90,41.934375,3958.729948364888)\" x=\"41.934375\" y=\"3958.729948364888\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Net exposure</text></g><g class=\"annotation\" data-index=\"0\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,407.5,124)\"><g class=\"cursor-pointer\" transform=\"translate(379,112)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"57\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"29.046875\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Orders</text></g></g></g><g class=\"annotation\" data-index=\"1\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,407.5,408.5738382099826)\"><g class=\"cursor-pointer\" transform=\"translate(379,397)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"57\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"29.078125\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Trades</text></g></g></g><g class=\"annotation\" data-index=\"2\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,407.5,693.1476764199653)\"><g class=\"cursor-pointer\" transform=\"translate(366,681)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"83\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"42.0625\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Trade PnL</text></g></g></g><g class=\"annotation\" data-index=\"3\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,407.5,977.7215146299483)\"><g class=\"cursor-pointer\" transform=\"translate(363,966)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"88\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"44.734375\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Asset Flow</text></g></g></g><g class=\"annotation\" data-index=\"4\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,407.5,1262.295352839931)\"><g class=\"cursor-pointer\" transform=\"translate(365,1250)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"84\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"42.640625\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Cash Flow</text></g></g></g><g class=\"annotation\" data-index=\"5\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,407.5,1546.869191049914)\"><g class=\"cursor-pointer\" transform=\"translate(380,1535)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"55\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"27.890625\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Assets</text></g></g></g><g class=\"annotation\" data-index=\"6\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,407.5,1831.4430292598965)\"><g class=\"cursor-pointer\" transform=\"translate(386,1819)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"42\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"21.625\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Cash</text></g></g></g><g class=\"annotation\" data-index=\"7\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,407.5,2116.016867469879)\"><g class=\"cursor-pointer\" transform=\"translate(359,2104)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"97\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"48.84375\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Asset Value</text></g></g></g><g class=\"annotation\" data-index=\"8\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,407.5,2400.5907056798624)\"><g class=\"cursor-pointer\" transform=\"translate(383,2389)\"><rect 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      "Subplot 'trades' does not support grouped data\n",
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      "/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/generic/plots_builder.py:340: UserWarning:\n",
      "\n",
      "Subplot 'trade_pnl' does not support grouped data\n",
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      "Subplot 'asset_flow' does not support grouped data\n",
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      "/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/generic/plots_builder.py:340: UserWarning:\n",
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style=\"vector-effect: non-scaling-stroke; fill: none; stroke: rgb(0, 0, 0); stroke-opacity: 0; stroke-width: 0px; opacity: 1;\"/></g><g class=\"points\"><path class=\"point\" transform=\"translate(35.89,16.16)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><path class=\"point\" transform=\"translate(177.19,16.16)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><path class=\"point\" transform=\"translate(318.5,16.16)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><path class=\"point\" transform=\"translate(459.81,16.16)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><path class=\"point\" transform=\"translate(601.11,16.16)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"text\"/></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"x9tick\"><text text-anchor=\"middle\" x=\"0\" y=\"2645\" transform=\"translate(118.89,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\"><tspan class=\"line\" dy=\"0em\" x=\"0\" y=\"2645\">Jan 1</tspan><tspan class=\"line\" dy=\"1.3em\" x=\"0\" y=\"2645\">2020</tspan></text></g><g class=\"x9tick\"><text text-anchor=\"middle\" x=\"0\" y=\"2645\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(260.19,0)\">Jan 2</text></g><g class=\"x9tick\"><text text-anchor=\"middle\" x=\"0\" y=\"2645\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(401.5,0)\">Jan 3</text></g><g class=\"x9tick\"><text text-anchor=\"middle\" x=\"0\" y=\"2645\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(542.81,0)\">Jan 4</text></g><g class=\"x9tick\"><text text-anchor=\"middle\" x=\"0\" y=\"2645\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(684.11,0)\">Jan 5</text></g></g><g class=\"yaxislayer-above\"><g class=\"y9tick\"><text text-anchor=\"end\" x=\"82\" y=\"4.199999999999999\" transform=\"translate(0,2619.253991769547)\" style=\"font-family: 'Open Sans', 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transform=\"translate(0,2487.683991769547)\">−0.01</text></g><g class=\"y9tick\"><text text-anchor=\"end\" x=\"82\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,2443.833991769547)\">−0.005</text></g><g class=\"y9tick\"><text text-anchor=\"end\" x=\"82\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,2399.973991769547)\">0</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"iciclelayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-65ca99\"><g class=\"clips\"/><clipPath id=\"legend65ca99\"><rect 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fill-rule=\"evenodd\" d=\"M83,971.3402469135801L720,971.3402469135801\" clip-path=\"url(#clip65ca99y4)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"4\" fill-rule=\"evenodd\" d=\"M83,1470.9769958847735L720,1470.9769958847735\" clip-path=\"url(#clip65ca99y5)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"6\" fill-rule=\"evenodd\" d=\"M83,1813.3604938271603L720,1813.3604938271603\" clip-path=\"url(#clip65ca99y7)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"7\" fill-rule=\"evenodd\" d=\"M83,2114.247242798354L720,2114.247242798354\" clip-path=\"url(#clip65ca99y8)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"8\" fill-rule=\"evenodd\" d=\"M83,2399.973991769547L720,2399.973991769547\" clip-path=\"url(#clip65ca99y9)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/></g></g><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(185,12.459259259259206)\"><rect class=\"bg\" shape-rendering=\"crispEdges\" width=\"535\" height=\"48\" x=\"0\" y=\"0\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; fill: rgb(255, 255, 255); fill-opacity: 1; stroke-width: 0px;\"/><g class=\"scrollbox\" transform=\"\" clip-path=\"url(#legend65ca99)\"><g class=\"groups\"><g class=\"traces\" transform=\"translate(0,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Cash</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(44, 160, 44); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(44, 160, 44); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"71.9375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(115.25,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Asset Value</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(23, 190, 207); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(23, 190, 207); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"112.75\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(230.5,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Value</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(148, 103, 189); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(148, 103, 189); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"75.953125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(345.75,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Benchmark</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(127, 127, 127); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(127, 127, 127); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"110.421875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(461,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Peak</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" 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      "/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/generic/plots_builder.py:340: UserWarning:\n",
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      "/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/generic/plots_builder.py:340: UserWarning:\n",
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stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"text\"/></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"x9tick\"><text text-anchor=\"middle\" x=\"0\" y=\"2645\" transform=\"translate(118.89,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\"><tspan class=\"line\" dy=\"0em\" x=\"0\" y=\"2645\">Jan 1</tspan><tspan class=\"line\" dy=\"1.3em\" x=\"0\" y=\"2645\">2020</tspan></text></g><g class=\"x9tick\"><text text-anchor=\"middle\" x=\"0\" y=\"2645\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(260.19,0)\">Jan 2</text></g><g class=\"x9tick\"><text text-anchor=\"middle\" x=\"0\" y=\"2645\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(401.5,0)\">Jan 3</text></g><g class=\"x9tick\"><text text-anchor=\"middle\" x=\"0\" y=\"2645\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(542.81,0)\">Jan 4</text></g><g class=\"x9tick\"><text text-anchor=\"middle\" x=\"0\" y=\"2645\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(684.11,0)\">Jan 5</text></g></g><g class=\"yaxislayer-above\"><g class=\"y9tick\"><text text-anchor=\"end\" x=\"82\" y=\"4.199999999999999\" transform=\"translate(0,2619.253991769547)\" style=\"font-family: 'Open Sans', 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transform=\"translate(0,2487.683991769547)\">−0.01</text></g><g class=\"y9tick\"><text text-anchor=\"end\" x=\"82\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,2443.833991769547)\">−0.005</text></g><g class=\"y9tick\"><text text-anchor=\"end\" x=\"82\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,2399.973991769547)\">0</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"iciclelayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-bdddc0\"><g class=\"clips\"/><clipPath id=\"legendbdddc0\"><rect width=\"535\" height=\"48\" x=\"0\" y=\"0\"/></clipPath></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"><path data-index=\"0\" fill-rule=\"evenodd\" d=\"M83,251.02L720,251.02\" clip-path=\"url(#clipbdddc0y)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"1\" fill-rule=\"evenodd\" d=\"M83,605.936748971193L720,605.936748971193\" clip-path=\"url(#clipbdddc0y2)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"2\" fill-rule=\"evenodd\" d=\"M83,685.6134979423865L720,685.6134979423865\" clip-path=\"url(#clipbdddc0y3)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"3\" fill-rule=\"evenodd\" d=\"M83,971.3402469135801L720,971.3402469135801\" clip-path=\"url(#clipbdddc0y4)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"4\" fill-rule=\"evenodd\" d=\"M83,1470.9769958847735L720,1470.9769958847735\" clip-path=\"url(#clipbdddc0y5)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"6\" fill-rule=\"evenodd\" d=\"M83,1813.3604938271603L720,1813.3604938271603\" clip-path=\"url(#clipbdddc0y7)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"7\" fill-rule=\"evenodd\" d=\"M83,2114.247242798354L720,2114.247242798354\" clip-path=\"url(#clipbdddc0y8)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"8\" fill-rule=\"evenodd\" d=\"M83,2399.973991769547L720,2399.973991769547\" clip-path=\"url(#clipbdddc0y9)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/></g></g><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(185,12.459259259259206)\"><rect class=\"bg\" shape-rendering=\"crispEdges\" width=\"535\" height=\"48\" x=\"0\" y=\"0\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; fill: rgb(255, 255, 255); fill-opacity: 1; stroke-width: 0px;\"/><g class=\"scrollbox\" transform=\"\" clip-path=\"url(#legendbdddc0)\"><g class=\"groups\"><g class=\"traces\" transform=\"translate(0,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Cash</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(44, 160, 44); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(44, 160, 44); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"71.9375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(115.25,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Asset Value</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(23, 190, 207); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(23, 190, 207); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"112.75\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(230.5,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Value</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(148, 103, 189); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(148, 103, 189); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"75.953125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(345.75,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Benchmark</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(127, 127, 127); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(127, 127, 127); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"110.421875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(461,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Peak</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M4.55,0L0,4.55L-4.55,0L0,-4.55Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(66, 133, 244); fill-opacity: 1; stroke: rgb(11, 84, 205); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"71.203125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Active</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M4.55,0L0,4.55L-4.55,0L0,-4.55Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(255, 170, 0); fill-opacity: 1; stroke: rgb(178, 118, 0); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"79.234375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(115.25,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Drawdown</text><g class=\"layers\"><g class=\"legendfill\"><path class=\"js-fill\" d=\"M5,0h30v6h-30z\" style=\"stroke-width: 0; fill: rgb(220, 57, 18); fill-opacity: 0.3;\"/></g><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(220, 57, 18); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(220, 57, 18); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"106.078125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(230.5,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Exposure</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(227, 119, 194); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(227, 119, 194); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"98.078125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g></g></g><rect class=\"scrollbar\" rx=\"20\" ry=\"3\" width=\"0\" height=\"0\" x=\"0\" y=\"0\" style=\"fill: rgb(128, 139, 164); fill-opacity: 1;\"/></g><g class=\"g-gtitle\"/><g class=\"g-xtitle\"/><g class=\"g-x2title\"/><g class=\"g-x3title\"/><g class=\"g-x4title\"/><g class=\"g-x5title\"/><g class=\"g-x6title\"/><g class=\"g-x7title\"/><g class=\"g-x8title\"/><g class=\"g-x9title\"><text class=\"x9title\" x=\"401.5\" y=\"2687.909375\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Index</text></g><g class=\"g-ytitle\"><text class=\"ytitle\" transform=\"rotate(-90,27.746875000000003,222.09300411522625)\" x=\"27.746875000000003\" y=\"222.09300411522625\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Cash flow</text></g><g class=\"g-y2title\"><text class=\"y2title\" transform=\"rotate(-90,22.309375000000003,507.8197530864194)\" x=\"22.309375000000003\" y=\"507.8197530864194\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Cash</text></g><g class=\"g-y3title\"><text class=\"y3title\" transform=\"rotate(-90,39.746875,793.546502057613)\" x=\"39.746875\" y=\"793.546502057613\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Asset value</text></g><g class=\"g-y4title\"><text class=\"y4title\" transform=\"rotate(-90,34.309375,1079.2732510288065)\" x=\"34.309375\" y=\"1079.2732510288065\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Value</text></g><g class=\"g-y5title\"><text class=\"y5title\" transform=\"rotate(-90,37.575,1365)\" x=\"37.575\" y=\"1365\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Cumulative returns</text></g><g class=\"g-y6title\"><text class=\"y6title\" transform=\"rotate(-90,34.309375,1650.7267489711933)\" x=\"34.309375\" y=\"1650.7267489711933\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Value</text></g><g class=\"g-y7title\"><text class=\"y7title\" transform=\"rotate(-90,26.825000000000003,1936.4534979423865)\" x=\"26.825000000000003\" y=\"1936.4534979423865\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Drawdown</text></g><g class=\"g-y8title\"><text class=\"y8title\" transform=\"rotate(-90,37.575,2222.1802469135805)\" x=\"37.575\" y=\"2222.1802469135805\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Gross exposure</text></g><g class=\"g-y9title\" transform=\"translate(1.50390625,0)\"><text class=\"y9title\" transform=\"rotate(-90,12.496875000000003,2507.9069958847736)\" x=\"12.496875000000003\" y=\"2507.9069958847736\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Net exposure</text></g><g class=\"annotation\" data-index=\"0\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,401.5,86)\"><g class=\"cursor-pointer\" transform=\"translate(359,74)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"84\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"42.640625\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Cash Flow</text></g></g></g><g class=\"annotation\" data-index=\"1\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,401.5,371.726748971193)\"><g class=\"cursor-pointer\" transform=\"translate(380,360)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"42\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"21.625\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Cash</text></g></g></g><g class=\"annotation\" data-index=\"2\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,401.5,657.4534979423865)\"><g class=\"cursor-pointer\" transform=\"translate(353,645)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"97\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"48.84375\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Asset Value</text></g></g></g><g class=\"annotation\" data-index=\"3\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,401.5,943.1802469135802)\"><g class=\"cursor-pointer\" transform=\"translate(377,931)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"48\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"24.296875\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Value</text></g></g></g><g class=\"annotation\" data-index=\"4\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,401.5,1228.9069958847736)\"><g class=\"cursor-pointer\" transform=\"translate(320,1217)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"162\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"81.34375\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Cumulative Returns</text></g></g></g><g class=\"annotation\" data-index=\"5\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,401.5,1514.633744855967)\"><g class=\"cursor-pointer\" transform=\"translate(353,1503)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"96\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"48.546875\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Drawdowns</text></g></g></g><g class=\"annotation\" data-index=\"6\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,401.5,1800.3604938271603)\"><g class=\"cursor-pointer\" transform=\"translate(353,1788)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"97\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"48.765625\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Underwater</text></g></g></g><g class=\"annotation\" data-index=\"7\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,401.5,2086.087242798354)\"><g class=\"cursor-pointer\" transform=\"translate(337,2074)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"128\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"64.671875\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Gross Exposure</text></g></g></g><g class=\"annotation\" data-index=\"8\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,401.5,2371.813991769547)\"><g class=\"cursor-pointer\" transform=\"translate(346,2360)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"111\" height=\"23\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"55.765625\" y=\"18\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 16px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Net Exposure</text></g></g></g></g></svg>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_shared.plot(subplots='all', column='first').show_svg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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class=\"point\" transform=\"translate(588.75,230.46)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"text\"/></g></g></g><g class=\"overplot\"/><path class=\"xlines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><path class=\"ylines-above crisp\" d=\"M0,0\" style=\"fill: none;\"/><g class=\"overlines-above\"/><g class=\"xaxislayer-above\"><g class=\"x14tick\"><text text-anchor=\"middle\" x=\"0\" y=\"4095\" transform=\"translate(130.21,0)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\"><tspan class=\"line\" dy=\"0em\" x=\"0\" y=\"4095\">Jan 1</tspan><tspan class=\"line\" dy=\"1.3em\" x=\"0\" y=\"4095\">2020</tspan></text></g><g class=\"x14tick\"><text text-anchor=\"middle\" x=\"0\" y=\"4095\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(268.6,0)\">Jan 2</text></g><g class=\"x14tick\"><text text-anchor=\"middle\" x=\"0\" y=\"4095\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(406.98,0)\">Jan 3</text></g><g class=\"x14tick\"><text text-anchor=\"middle\" x=\"0\" y=\"4095\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(545.37,0)\">Jan 4</text></g><g class=\"x14tick\"><text text-anchor=\"middle\" x=\"0\" y=\"4095\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(683.75,0)\">Jan 5</text></g></g><g class=\"yaxislayer-above\"><g class=\"y14tick\"><text text-anchor=\"end\" x=\"94\" y=\"4.199999999999999\" transform=\"translate(0,4065.919896729776)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\">0</text></g><g class=\"y14tick\"><text text-anchor=\"end\" x=\"94\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,4023.109896729776)\">0.005</text></g><g class=\"y14tick\"><text text-anchor=\"end\" x=\"94\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,3980.2998967297763)\">0.01</text></g><g class=\"y14tick\"><text text-anchor=\"end\" x=\"94\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,3937.4898967297763)\">0.015</text></g><g class=\"y14tick\"><text text-anchor=\"end\" x=\"94\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,3894.679896729776)\">0.02</text></g><g class=\"y14tick\"><text text-anchor=\"end\" x=\"94\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,3851.869896729776)\">0.025</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"iciclelayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-fa8056\"><g class=\"clips\"/><clipPath id=\"legendfa8056\"><rect width=\"609\" height=\"86\" x=\"0\" y=\"0\"/></clipPath></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"><path data-index=\"2\" fill-rule=\"evenodd\" d=\"M95,706.1476764199653L720,706.1476764199653\" clip-path=\"url(#clipfa8056y3)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"3\" fill-rule=\"evenodd\" d=\"M95,1200.6915146299482L720,1200.6915146299482\" clip-path=\"url(#clipfa8056y4)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"4\" fill-rule=\"evenodd\" d=\"M95,1298.085352839931L720,1298.085352839931\" clip-path=\"url(#clipfa8056y5)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"5\" fill-rule=\"evenodd\" d=\"M95,1789.329191049914L720,1789.329191049914\" clip-path=\"url(#clipfa8056y6)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"6\" fill-rule=\"evenodd\" d=\"M95,1859.5230292598965L720,1859.5230292598965\" clip-path=\"url(#clipfa8056y7)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"7\" fill-rule=\"evenodd\" d=\"M95,2358.476867469879L720,2358.476867469879\" clip-path=\"url(#clipfa8056y8)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"8\" fill-rule=\"evenodd\" d=\"M95,2428.6707056798623L720,2428.6707056798623\" clip-path=\"url(#clipfa8056y9)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"9\" fill-rule=\"evenodd\" d=\"M95,2927.4045438898456L720,2927.4045438898456\" clip-path=\"url(#clipfa8056y10)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"11\" fill-rule=\"evenodd\" d=\"M95,3267.3122203098105L720,3267.3122203098105\" clip-path=\"url(#clipfa8056y12)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"12\" fill-rule=\"evenodd\" d=\"M95,3566.966058519793L720,3566.966058519793\" clip-path=\"url(#clipfa8056y13)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/><path data-index=\"13\" fill-rule=\"evenodd\" d=\"M95,4065.919896729776L720,4065.919896729776\" clip-path=\"url(#clipfa8056y14)\" style=\"opacity: 1; stroke: rgb(128, 128, 128); stroke-opacity: 1; fill: rgb(0, 0, 0); fill-opacity: 0; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/></g></g><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(111,11.966265060240659)\"><rect class=\"bg\" shape-rendering=\"crispEdges\" width=\"609\" height=\"86\" x=\"0\" y=\"0\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; fill: rgb(255, 255, 255); fill-opacity: 1; stroke-width: 0px;\"/><g class=\"scrollbox\" transform=\"\" clip-path=\"url(#legendfa8056)\"><g class=\"groups\"><g class=\"traces\" transform=\"translate(0,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Close</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(31, 119, 180); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(31, 119, 180); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"74.859375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(125.1875,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Buy</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M-4.62,2H4.62L0,-4Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(55, 177, 63); fill-opacity: 1; stroke: rgb(38, 123, 44); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"65.421875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(250.375,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Sell</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M-4.62,-2H4.62L0,4Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(234, 67, 53); fill-opacity: 1; stroke: rgb(181, 31, 18); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"64.4375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(375.5625,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Entry</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3.5,3.5H-3.5V-3.5H3.5Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(66, 133, 244); fill-opacity: 1; stroke: rgb(11, 84, 205); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"74.640625\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(500.75,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Exit - Loss</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3.5,3.5H-3.5V-3.5H3.5Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(234, 67, 53); fill-opacity: 1; stroke: rgb(181, 31, 18); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"105.578125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Active</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3.5,3.5H-3.5V-3.5H3.5Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(255, 170, 0); fill-opacity: 1; stroke: rgb(178, 118, 0); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"79.234375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(125.1875,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Closed - Loss</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M7,0A7,7 0 1,1 0,-7A7,7 0 0,1 7,0Z\" style=\"opacity: 0.9; stroke-width: 1px; fill: rgb(234, 67, 53); fill-opacity: 1; stroke: rgb(181, 31, 18); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"122.6875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(250.375,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Open</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3.5,0A3.5,3.5 0 1,1 0,-3.5A3.5,3.5 0 0,1 3.5,0Z\" style=\"opacity: 0.75; stroke-width: 1px; fill: rgb(255, 170, 0); fill-opacity: 1; stroke: rgb(178, 118, 0); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"74.171875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(375.5625,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Assets</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(140, 86, 75); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(140, 86, 75); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"81.34375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(500.75,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Cash</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(44, 160, 44); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(44, 160, 44); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"71.9375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Asset Value</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(23, 190, 207); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(23, 190, 207); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"112.75\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(125.1875,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Value</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(148, 103, 189); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(148, 103, 189); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"75.953125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(250.375,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Benchmark</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(127, 127, 127); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(127, 127, 127); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"110.421875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(375.5625,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Peak</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M4.55,0L0,4.55L-4.55,0L0,-4.55Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(66, 133, 244); fill-opacity: 1; stroke: rgb(11, 84, 205); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"71.203125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(500.75,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Active</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M4.55,0L0,4.55L-4.55,0L0,-4.55Z\" style=\"opacity: 1; stroke-width: 1px; fill: rgb(255, 170, 0); fill-opacity: 1; stroke: rgb(178, 118, 0); stroke-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"79.234375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,71.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Drawdown</text><g class=\"layers\"><g class=\"legendfill\"><path class=\"js-fill\" d=\"M5,0h30v6h-30z\" style=\"stroke-width: 0; fill: rgb(220, 57, 18); fill-opacity: 0.3;\"/></g><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(220, 57, 18); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(220, 57, 18); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"106.078125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(125.1875,71.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_shared.plot(subplots='all', column='a', group_by=False).show_svg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from vectorbt.utils.colors import adjust_opacity\n",
    "\n",
    "portfolio['a'].plot(\n",
    "    subplots=['drawdowns', 'underwater'],\n",
    "    subplot_settings=dict(\n",
    "        drawdowns=dict(top_n=3),\n",
    "        underwater=dict(\n",
    "            trace_kwargs=dict(\n",
    "                line_color='#FF6F00',\n",
    "                fillcolor=adjust_opacity('#FF6F00', 0.3)\n",
    "            )\n",
    "        )\n",
    "    )\n",
    ").show_svg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from vectorbt import settings\n",
    "color_schema = settings['plotting']['color_schema']\n",
    "\n",
    "fig = portfolio['a'].plot(subplots=[\n",
    "    'orders',\n",
    "    ('order_size', dict(title='Order Size', check_is_not_grouped=True))  # placeholder\n",
    "])\n",
    "\n",
    "order_size.vbt.plot(trace_kwargs=dict(name='Order Size'), add_trace_kwargs=dict(row=2, col=1), fig=fig).show_svg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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